Showing papers on "Speaker recognition published in 2015"

Speaker Recognition by Machines and Humans: A tutorial review

Abstract: Identifying a person by his or her voice is an important human trait most take for granted in natural human-to-human interaction/communication. Speaking to someone over the telephone usually begins by identifying who is speaking and, at least in cases of familiar speakers, a subjective verification by the listener that the identity is correct and the conversation can proceed. Automatic speaker-recognition systems have emerged as an important means of verifying identity in many e-commerce applications as well as in general business interactions, forensics, and law enforcement. Human experts trained in forensic speaker recognition can perform this task even better by examining a set of acoustic, prosodic, and linguistic characteristics of speech in a general approach referred to as structured listening. Techniques in forensic speaker recognition have been developed for many years by forensic speech scientists and linguists to help reduce any potential bias or preconceived understanding as to the validity of an unknown audio sample and a reference template from a potential suspect. Experienced researchers in signal processing and machine learning continue to develop automatic algorithms to effectively perform speaker recognition?with ever-improving performance?to the point where automatic systems start to perform on par with human listeners. In this article, we review the literature on speaker recognition by machines and humans, with an emphasis on prominent speaker-modeling techniques that have emerged in the last decade for automatic systems. We discuss different aspects of automatic systems, including voice-activity detection (VAD), features, speaker models, standard evaluation data sets, and performance metrics. Human speaker recognition is discussed in two parts?the first part involves forensic speaker-recognition methods, and the second illustrates how a na?ve listener performs this task from a neuroscience perspective. We conclude this review with a comparative study of human versus machine speaker recognition and attempt to point out strengths and weaknesses of each.

Deep Neural Network Approaches to Speaker and Language Recognition

Abstract: The impressive gains in performance obtained using deep neural networks (DNNs) for automatic speech recognition (ASR) have motivated the application of DNNs to other speech technologies such as speaker recognition (SR) and language recognition (LR). Prior work has shown performance gains for separate SR and LR tasks using DNNs for direct classification or for feature extraction. In this work we present the application of single DNN for both SR and LR using the 2013 Domain Adaptation Challenge speaker recognition (DAC13) and the NIST 2011 language recognition evaluation (LRE11) benchmarks. Using a single DNN trained for ASR on Switchboard data we demonstrate large gains on performance in both benchmarks: a 55% reduction in EER for the DAC13 out-of-domain condition and a 48% reduction in ${C_{avg}}$ on the LRE11 30 s test condition. It is also shown that further gains are possible using score or feature fusion leading to the possibility of a single i-vector extractor producing state-of-the-art SR and LR performance

Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011

Abstract: Speaker emotion recognition is achieved through processing methods that include isolation of the speech signal and extraction of selected features for the final classification. In terms of acoustics, speech processing techniques offer extremely valuable paralinguistic information derived mainly from prosodic and spectral features. In some cases, the process is assisted by speech recognition systems, which contribute to the classification using linguistic information. Both frameworks deal with a very challenging problem, as emotional states do not have clear-cut boundaries and often differ from person to person. In this article, research papers that investigate emotion recognition from audio channels are surveyed and classified, based mostly on extracted and selected features and their classification methodology. Important topics from different classification techniques, such as databases available for experimentation, appropriate feature extraction and selection methods, classifiers and performance issues are discussed, with emphasis on research published in the last decade. This survey also provides a discussion on open trends, along with directions for future research on this topic.

Speech Emotion Recognition Using Fourier Parameters

Abstract: Recently, studies have been performed on harmony features for speech emotion recognition. It is found in our study that the first- and second-order differences of harmony features also play an important role in speech emotion recognition. Therefore, we propose a new Fourier parameter model using the perceptual content of voice quality and the first- and second-order differences for speaker-independent speech emotion recognition. Experimental results show that the proposed Fourier parameter (FP) features are effective in identifying various emotional states in speech signals. They improve the recognition rates over the methods using Mel frequency cepstral coefficient (MFCC) features by 16.2, 6.8 and 16.6 points on the German database (EMODB), Chinese language database (CASIA) and Chinese elderly emotion database (EESDB). In particular, when combining FP with MFCC, the recognition rates can be further improved on the aforementioned databases by 17.5, 10 and 10.5 points, respectively.

The NTT CHiME-3 system: Advances in speech enhancement and recognition for mobile multi-microphone devices

Abstract: CHiME-3 is a research community challenge organised in 2015 to evaluate speech recognition systems for mobile multi-microphone devices used in noisy daily environments. This paper describes NTT's CHiME-3 system, which integrates advanced speech enhancement and recognition techniques. Newly developed techniques include the use of spectral masks for acoustic beam-steering vector estimation and acoustic modelling with deep convolutional neural networks based on the "network in network" concept. In addition to these improvements, our system has several key differences from the official baseline system. The differences include multi-microphone training, dereverberation, and cross adaptation of neural networks with different architectures. The impacts that these techniques have on recognition performance are investigated. By combining these advanced techniques, our system achieves a 3.45% development error rate and a 5.83% evaluation error rate. Three simpler systems are also developed to perform evaluations with constrained set-ups.

Convolutional Neural Networks-based continuous speech recognition using raw speech signal

Abstract: State-of-the-art automatic speech recognition systems model the relationship between acoustic speech signal and phone classes in two stages, namely, extraction of spectral-based features based on prior knowledge followed by training of acoustic model, typically an artificial neural network (ANN). In our recent work, it was shown that Convolutional Neural Networks (CNNs) can model phone classes from raw acoustic speech signal, reaching performance on par with other existing feature-based approaches. This paper extends the CNN-based approach to large vocabulary speech recognition task. More precisely, we compare the CNN-based approach against the conventional ANN-based approach on Wall Street Journal corpus. Our studies show that the CNN-based approach achieves better performance than the conventional ANN-based approach with as many parameters. We also show that the features learned from raw speech by the CNN-based approach could generalize across different databases.

Emotion recognition of affective speech based on multiple classifiers using acoustic-prosodic information and semantic labels (Extended abstract)

Abstract: This work presents an approach to emotion recognition of affective speech based on multiple classifiers using acoustic-prosodic information (AP) and semantic labels (SLs). For AP-based recognition, acoustic and prosodic features are extracted from the detected emotional salient segments of the input speech. Three types of models GMMs, SVMs, and MLPs are adopted as the base-level classifiers. A Meta Decision Tree (MDT) is then employed for classifier fusion to obtain the AP-based emotion recognition confidence. For SL-based recognition, semantic labels are used to automatically extract Emotion Association Rules (EARs) from the recognized word sequence of the affective speech. The maximum entropy model (MaxEnt) is thereafter utilized to characterize the relationship between emotional states and EARs for emotion recognition. Finally, a weighted product fusion method is used to integrate the AP-based and SL-based recognition results for final emotion decision. For evaluation, 2,033 utterances for four emotional states were collected. The experimental results reveal that the emotion recognition performance for AP-based recognition using MDT achieved 80.00%. On the other hand, an average recognition accuracy of 80.92% was obtained for SL-based recognition. Finally, combining AP information and SLs achieved 83.55% accuracy for emotion recognition.

The RedDots Data Collection for Speaker Recognition

Abstract: This paper describes data collection efforts conducted as part of the RedDots project which is dedicated to the study of speaker recognition under conditions where test utterances are of short duration and of variable phonetic content. At the current stage, we focus on English speakers, both native and non-native, recruited worldwide. This is made possible through the use of a recording front-end consisting of an application running on mobile devices communicating with a centralized web server at the back-end. Speech recordings are collected by having speakers read text prompts displayed on the screen of the mobile devices. We aim to collect a large number of sessions from each speaker over a long time span, typically one session per week over a one year period. The corpus is expected to include rich inter-speaker and intra-speaker variations, both intrinsic and extrinsic (that is, due to recording channel and acoustic environment).

A Unified Deep Neural Network for Speaker and Language Recognition

Abstract: Learned feature representations and sub-phoneme posteriors from Deep Neural Networks (DNNs) have been used separately to produce significant performance gains for speaker and language recognition tasks. In this work we show how these gains are possible using a single DNN for both speaker and language recognition. The unified DNN approach is shown to yield substantial performance improvements on the the 2013 Domain Adaptation Challenge speaker recognition task (55% reduction in EER for the out-of-domain condition) and on the NIST 2011 Language Recognition Evaluation (48% reduction in EER for the 30s test condition).

Speaker identification and unsupervised speaker adaptation techniques

Abstract: Systems and processes for generating a speaker profile for use in performing speaker identification for a virtual assistant are provided. One example process can include receiving an audio input including user speech and determining whether a speaker of the user speech is a predetermined user based on a speaker profile for the predetermined user. In response to determining that the speaker of the user speech is the predetermined user, the user speech can be added to the speaker profile and operation of the virtual assistant can be triggered. In response to determining that the speaker of the user speech is not the predetermined user, the user speech can be added to an alternate speaker profile and operation of the virtual assistant may not be triggered. In some examples, contextual information can be used to verify results produced by the speaker identification process.

Time delay deep neural network-based universal background models for speaker recognition

Abstract: Recently, deep neural networks (DNN) have been incorporated into i-vector-based speaker recognition systems, where they have significantly improved state-of-the-art performance. In these systems, a DNN is used to collect sufficient statistics for i-vector extraction. In this study, the DNN is a recently developed time delay deep neural network (TDNN) that has achieved promising results in LVCSR tasks. We believe that the TDNN-based system achieves the best reported results on SRE10 and it obtains a 50% relative improvement over our GMM baseline in terms of equal error rate (EER). For some applications, the computational cost of a DNN is high. Therefore, we also investigate a lightweight alternative in which a supervised GMM is derived from the TDNN posteriors. This method maintains the speed of the traditional unsupervised-GMM, but achieves a 20% relative improvement in EER.

The MGB challenge: Evaluating multi-genre broadcast media recognition

Abstract: This paper describes the Multi-Genre Broadcast (MGB) Challenge at ASRU 2015, an evaluation focused on speech recognition, speaker diarization, and "lightly supervised" alignment of BBC TV recordings. The challenge training data covered the whole range of seven weeks BBC TV output across four channels, resulting in about 1,600 hours of broadcast audio. In addition several hundred million words of BBC subtitle text was provided for language modelling. A novel aspect of the evaluation was the exploration of speech recognition and speaker diarization in a longitudinal setting — i.e. recognition of several episodes of the same show, and speaker diarization across these episodes, linking speakers. The longitudinal tasks also offered the opportunity for systems to make use of supplied metadata including show title, genre tag, and date/time of transmission. This paper describes the task data and evaluation process used in the MGB challenge, and summarises the results obtained.

Advances in deep neural network approaches to speaker recognition

Abstract: The recent application of deep neural networks (DNN) to speaker identification (SID) has resulted in significant improvements over current state-of-the-art on telephone speech. In this work, we report a similar achievement in DNN-based SID performance on microphone speech. We consider two approaches to DNN-based SID: one that uses the DNN to extract features, and another that uses the DNN during feature modeling. Modeling is conducted using the DNN/i-vector framework, in which the traditional universal background model is replaced with a DNN. The recently proposed use of bottleneck features extracted from a DNN is also evaluated. Systems are first compared with a conventional universal background model (UBM) Gaussian mixture model (GMM) i-vector system on the clean conditions of the NIST 2012 speaker recognition evaluation corpus, where a lack of robustness to microphone speech is found. Several methods of DNN feature processing are then applied to bring significantly greater robustness to microphone speech. To direct future research, the DNN-based systems are also evaluated in the context of audio degradations including noise and reverberation.

Multi-speaker modeling and speaker adaptation for DNN-based TTS synthesis

Abstract: In DNN-based TTS synthesis, DNNs hidden layers can be viewed as deep transformation for linguistic features and the output layers as representation of acoustic space to regress the transformed linguistic features to acoustic parameters. The deep-layered architectures of DNN can not only represent highly-complex transformation compactly, but also take advantage of huge amount of training data. In this paper, we propose an approach to model multiple speakers TTS with a general DNN, where the same hidden layers are shared among different speakers while the output layers are composed of speaker-dependent nodes explaining the target of each speaker. The experimental results show that our approach can significantly improve the quality of synthesized speech objectively and subjectively, comparing with speech synthesized from the individual, speaker-dependent DNN-based TTS. We further transfer the hidden layers for a new speaker with limited training data and the resultant synthesized speech of the new speaker can also achieve a good quality in term of naturalness and speaker similarity.

Speaker adaptive training of deep neural network acoustic models using i-vectors

Abstract: In acoustic modeling, speaker adaptive training (SAT) has been a long-standing technique for the traditional Gaussian mixture models (GMMs). Acoustic models trained with SAT become independent of training speakers and generalize better to unseen testing speakers. This paper ports the idea of SAT to deep neural networks (DNNs), and proposes a framework to perform feature-space SAT for DNNs. Using i-vectors as speaker representations, our framework learns an adaptation neural network to derive speaker-normalized features. Speaker adaptive models are obtained by fine-tuning DNNs in such a feature space. This framework can be applied to various feature types and network structures, posing a very general SAT solution. In this paper, we fully investigate how to build SAT-DNN models effectively and efficiently. First, we study the optimal configurations of SAT-DNNs for large-scale acoustic modeling tasks. Then, after presenting detailed comparisons between SAT-DNNs and the existing DNN adaptation methods, we propose to combine SAT-DNNs and model-space DNN adaptation during decoding. Finally, to accelerate learning of SAT-DNNs, a simple yet effective strategy, frame skipping, is employed to reduce the size of training data. Our experiments show that compared with a strong DNN baseline, the SAT-DNN model achieves 13.5% and 17.5% relative improvement on word error rates (WERs), without and with model-space adaptation applied respectively. Data reduction based on frame skipping results in 2 $ \times $ speed-up for SAT-DNN training, while causing negligible WER loss on the testing data.

Deep neural networks for single-channel multi-talker speech recognition

Abstract: We investigate techniques based on deep neural networks (DNNs) for attacking the single-channel multi-talker speech recognition problem. Our proposed approach contains five key ingredients: a multi-style training strategy on artificially mixed speech data, a separate DNN to estimate senone posterior probabilities of the louder and softer speakers at each frame, a weighted finite-state transducer (WFST)-based two-talker decoder to jointly estimate and correlate the speaker and speech, a speaker switching penalty estimated from the energy pattern change in the mixed-speech, and a confidence based system combination strategy. Experiments on the 2006 speech separation and recognition challenge task demonstrate that our proposed DNN-based system has remarkable noise robustness to the interference of a competing speaker. The best setup of our proposed systems achieves an average word error rate (WER) of 18.8% across different SNRs and outperforms the state-of-the-art IBM superhuman system by 2.8% absolute with fewer assumptions.

Providing an indication of the suitability of speech recognition

Abstract: This relates to providing an indication of the suitability of an acoustic environment for performing speech recognition. One process can include receiving an audio input and determining a speech recognition suitability based on the audio input. The speech recognition suitability can include a numerical, textual, graphical, or other representation of the suitability of an acoustic environment for performing speech recognition. The process can further include displaying a visual representation of the speech recognition suitability to indicate the likelihood that a spoken user input will be interpreted correctly. This allows a user to determine whether to proceed with the performance of a speech recognition process, or to move to a different location having a better acoustic environment before performing the speech recognition process. In some examples, the user device can disable operation of a speech recognition process in response to determining that the speech recognition suitability is below a threshold suitability.

Dynamic threshold for speaker verification

Abstract: The invention relates to the dynamic threshold for speaker verification. Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a dynamic threshold forspeaker verification are disclosed. In one aspect, a method includes the actions of receiving, for each of multiple utterances of a hot word, a data set including at least a speaker verification confidence score, and environmental context data. The actions further include selecting from among the data sets, a subset of the data sets that are associated with a particular environmental context. Theactions further include selecting a particular data set from among the subset of data sets based on one or more selection criteria. The actions further include selecting, as a speaker verification threshold for the particular environmental context, the speaker verification confidence score. The actions further include providing the speaker verification threshold for use in performing speaker verification of utterances that are associated with the particular environmental context.

Automatic speech recognition based on user feedback

Abstract: Systems and processes for processing speech in a digital assistant are provided. In one example process, a first speech input can be received from a user. The first speech input can be processed using a first automatic speech recognition system to produce a first recognition result. An input indicative of a potential error in the first recognition result can be received. The input can be used to improve the first recognition result. For example, the input can include a second speech input that is a repetition of the first speech input. The second speech input can be processed using a second automatic speech recognition system to produce a second recognition result.

Locally-Connected and Convolutional Neural Networks for Small Footprint Speaker Recognition

Abstract: This work compares the performance of deep LocallyConnected Networks (LCN) and Convolutional Neural Networks (CNN) for text-dependent speaker recognition. These topologies model the local time-frequency correlations of the speech signal better, using only a fraction of the number of parameters of a fully connected Deep Neural Network (DNN) used in previous works. We show that both a LCN and CNN can reduce the total model footprint to 30% of the original size compared to a baseline fully-connected DNN, with minimal impact in performance or latency. In addition, when matching parameters, the LCN improves speaker verification performance, as measured by equal error rate (EER), by 8% relative over the baseline without increasing model size or computation. Similarly, a CNN improves EER by 10% relative over the baseline for the same model size but with increased computation.

Multi-task learning for text-dependent speaker verification.

Abstract: Text-dependent speaker verification uses short utterances and verifies both speaker identity and text contents. Due to this nature, traditional state-of-the-art speaker verification approaches, such as i-vector, may not work well. Recently, there has been interest of applying deep learning to speaker verification, however in previous works, standalone deep learning systems have not achieved state-of-the-art performance and they have to be used in system combination or as tandem features to obtain gains. In this paper, a novel multi-task deep learning framework is proposed for text-dependent speaker verification. First, multi-task deep learning is employed to learn both speaker identity and text information. With the learned network, utterance level average of the outputs of the last hidden layer, referred to as j-vector, means joint-vector, is extracted. Discriminant function, with classes defined as multi-task labels on both speaker and text, is then applied to the j-vectors as the decision function for the closed-set recognition, and Probabilistic Linear Discriminant Analysis (PLDA), with classes defined as on the multi-task labels, is applied to the j-vectors for the verification. Experiments on the RSR2015 corpus showed that the j-vector approach leads to good result on the evaluation data. The proposed multi-task deep learning system achieved 0.54% EER, 0.14% EER for the closed-set condition.

Joint Speaker Verification and Antispoofing in the $i$ -Vector Space

Abstract: Any biometric recognizer is vulnerable to spoofing attacks and hence voice biometric, also called automatic speaker verification (ASV), is no exception; replay, synthesis, and conversion attacks all provoke false acceptances unless countermeasures are used. We focus on voice conversion (VC) attacks considered as one of the most challenging for modern recognition systems. To detect spoofing, most existing countermeasures assume explicit or implicit knowledge of a particular VC system and focus on designing discriminative features. In this paper, we explore back-end generative models for more generalized countermeasures. In particular, we model synthesis-channel subspace to perform speaker verification and antispoofing jointly in the ${i}$ -vector space, which is a well-established technique for speaker modeling. It enables us to integrate speaker verification and antispoofing tasks into one system without any fusion techniques. To validate the proposed approach, we study vocoder-matched and vocoder-mismatched ASV and VC spoofing detection on the NIST 2006 speaker recognition evaluation data set. Promising results are obtained for standalone countermeasures as well as their combination with ASV systems using score fusion and joint approach.

Joint training of front-end and back-end deep neural networks for robust speech recognition

Abstract: Based on the recently proposed speech pre-processing front-end with deep neural networks (DNNs), we first investigate different feature mapping directly from noisy speech via DNN for robust speech recognition. Next, we propose to jointly train a single DNN for both feature mapping and acoustic modeling. In the end, we show that the word error rate (WER) of the jointly trained system could be significantly reduced by the fusion of multiple DNN pre-processing systems which implies that features obtained from different domains of the DNN-enhanced speech signals are strongly complementary. Testing on the Aurora4 noisy speech recognition task our best system with multi-condition training can achieves an average WER of 10.3%, yielding a relative reduction of 16.3% over our previous DNN pre-processing only system with a WER of 12.3%. To the best of our knowledge, this represents the best published result on the Aurora4 task without using any adaptation techniques.

Robust end-pointing of speech signals using speaker recognition

Abstract: Systems and processes for robust end-pointing of speech signals using speaker recognition are provided. In one example process, a stream of audio having a spoken user request can be received. A first likelihood that the stream of audio includes user speech can be determined. A second likelihood that the stream of audio includes user speech spoken by an authorized user can be determined. A start-point or an end-point of the spoken user request can be determined based at least in part on the first likelihood and the second likelihood.

A unique approach in text independent speaker recognition using MFCC feature sets and probabilistic neural network

Abstract: This paper motivates the use of combination of mel frequency cepstral coefficients (MFCC) and its delta derivatives (DMFCC and DDMFCC) calculated using mel spaced Gaussian filter banks for text independent speaker recognition. MFCC modeled on the human auditory system shows robustness against noise and session changes and hence has become synonymous with speaker recognition. Our main aim is to test the accuracy of our proposed feature set for different values of frame overlap and MFCC feature vector sizes to identify the system having highest accuracy. Principal component analysis (PCA) is applied before the training and testing stages for feature dimensionality reduction thereby increasing computing speed and puts low constraint on the memory required for processing. The use of probabilistic neural network (PNN) in the modeling domain provided the advantages of achieving lower operational times during the training stages. The experiments examined the percentage identification accuracy (PIA) of MFCC, combination of MFCC and DMFCC as well as combination of all three feature sets MFCC, DMFCC and DDMFCC. The proposed feature set attains an identification accuracy of 94% for frame overlap of 90% and MFCC feature size of 18 coefficients. It outperforms the identification rates of the other two feature sets. These speaker recognition experiments were tested using the Voxforge database.

SAS: A speaker verification spoofing database containing diverse attacks

Abstract: This paper presents the first version of a speaker verification spoofing and anti-spoofing database, named SAS corpus. The corpus includes nine spoofing techniques, two of which are speech synthesis, and seven are voice conversion. We design two protocols, one for standard speaker verification evaluation, and the other for producing spoofing materials. Hence, they allow the speech synthesis community to produce spoofing materials incrementally without knowledge of speaker verification spoofing and anti-spoofing. To provide a set of preliminary results, we conducted speaker verification experiments using two state-of-the-art systems. Without any anti-spoofing techniques, the two systems are extremely vulnerable to the spoofing attacks implemented in our SAS corpus.

Supervised domain adaptation for emotion recognition from speech

Abstract: One of the main barriers in the deployment of speech emotion recognition systems in real applications is the lack of generalization of the emotion classifiers. The recognition performance achieved in controlled recordings drops when the models are tested with different speakers, channels, environments and domain conditions. This paper explores supervised model adaptation, which can improve the performance of systems evaluated with mismatched training and testing conditions. We address the following key questions in the context of supervised adaptation for speech emotion recognition: (a) how much labeled data is needed for adaptation to achieve good performance? (b) how important is speaker diversity in the labeled set? (c) can spontaneous acted data provide similar performance than naturalistic non-acted recordings? and (d) what is the best approach to adapt the models (domain adaptation versus incremental/online training)? We address these problems by using a multi-corpus framework where the models are trained and tested with different databases. The results indicate that even small portion of data used for adaptation can significantly improve the performance. Increasing the speaker diversity in the labeled data used for adaptation does not provide significant gain in performance. Also, we observe similar performance when the classifiers are trained with naturalistic non-acted data and spontaneous acted data.

Speaker identification using vowels features through a combined method of formants, wavelets, and neural network classifiers

Abstract: This paper proposes a new method for speaker feature extraction based on Formants, Wavelet Entropy and Neural Networks denoted as FWENN.In the first stage, five formants and seven Shannon entropy wavelet packets are extracted from the speakers' signals as the speaker feature vector.In the second stage, these 12 feature extraction coefficients are used as inputs to feed-forward neural networks.In contrast to conventional speaker identification methods that extract features from sentences (or words), the proposed method extracts the features from vowels.Advantages of using vowels include the ability to identify speakers when only partially-recorded words are available. This may be useful for deaf-mute persons. This paper proposes a new method for speaker feature extraction based on Formants, Wavelet Entropy and Neural Networks denoted as FWENN. In the first stage, five formants and seven Shannon entropy wavelet packet are extracted from the speakers' signals as the speaker feature vector. In the second stage, these 12 feature extraction coefficients are used as inputs to feed-forward neural networks. Probabilistic neural network is also proposed for comparison. In contrast to conventional speaker recognition methods that extract features from sentences (or words), the proposed method extracts the features from vowels. Advantages of using vowels include the ability to recognize speakers when only partially-recorded words are available. This may be useful for deaf-mute persons or when the recordings are damaged. Experimental results show that the proposed method succeeds in the speaker verification and identification tasks with high classification rate. This is accomplished with minimum amount of information, using only 12 coefficient features (i.e. vector length) and only one vowel signal, which is the major contribution of this work. The results are further compared to well-known classical algorithms for speaker recognition and are found to be superior.