Speaker recognition

About: Speaker recognition is a research topic. Over the lifetime, 14990 publications have been published within this topic receiving 310061 citations.

Lexical modeling of non-native speech for automatic speech recognition

Abstract: The paper examines the recognition of non-native speech in JUPITER, a speaker-independent, spontaneous-speech conversational system. Because the non-native speech in this domain is limited and varied, speaker- and accent-specific methods are impractical. We therefore chose to model all of the non-native data with a single model. In particular, the paper describes an attempt to better model non-native lexical patterns. These patterns are incorporated by applying context-independent phonetic confusion rules, whose probabilities are estimated from training data. Using this approach, the word error rate on a non-native test set is reduced from 20.9% to 18.8%.

Sparse Representation for Speaker Identification

Abstract: We address the closed-set problem of speaker identification by presenting a novel sparse representation classification algorithm. We propose to develop an over complete dictionary using the GMM mean super vector kernel for all the training utterances. A given test utterance corresponds to only a small fraction of the whole training database. We therefore propose to represent a given test utterance as a linear combination of all the training utterances, thereby generating a naturally sparse representation. Using this sparsity, the unknown vector of coefficients is computed via l1minimization which is also the sparsest solution [12]. Ideally, the vector of coefficients so obtained has nonzero entries representing the class index of the given test utterance. Experiments have been conducted on the standard TIMIT [14] database and a comparison with the state-of-art speaker identification algorithms yields a favorable performance index for the proposed algorithm.

Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends.

Abstract: Research on speech processing has traditionally considered the task of designing hand-engineered acoustic features (feature engineering) as a separate distinct problem from the task of designing efficient machine learning (ML) models to make prediction and classification decisions. There are two main drawbacks to this approach: firstly, the feature engineering being manual is cumbersome and requires human knowledge; and secondly, the designed features might not be best for the objective at hand. This has motivated the adoption of a recent trend in speech community towards utilisation of representation learning techniques, which can learn an intermediate representation of the input signal automatically that better suits the task at hand and hence lead to improved performance. The significance of representation learning has increased with advances in deep learning (DL), where the representations are more useful and less dependent on human knowledge, making it very conducive for tasks like classification, prediction, etc. The main contribution of this paper is to present an up-to-date and comprehensive survey on different techniques of speech representation learning by bringing together the scattered research across three distinct research areas including Automatic Speech Recognition (ASR), Speaker Recognition (SR), and Speaker Emotion Recognition (SER). Recent reviews in speech have been conducted for ASR, SR, and SER, however, none of these has focused on the representation learning from speech---a gap that our survey aims to bridge.

Adaptation in statistical pattern recognition using tangent vectors

Abstract: We integrate the tangent method into a statistical framework for classification analytically and practically. The resulting consistent framework for adaptation allows us to efficiently estimate the tangent vectors representing the variability. The framework improves classification results on two real-world pattern recognition tasks from the domains handwritten character recognition and automatic speech recognition.

Emotion recognition from Assamese speeches using MFCC features and GMM classifier

Abstract: This paper presents a method based on Gaussian mixture model (GMM) classifier and Mel-frequency cepstral coefficients (MFCC) as features for emotion recognition from Assamese speeches. For training and testing of the method, data collection is carried out in Jorhat (Assam, India), which consisted of acted speeches of one short emotionally biased sentence repeated 5 times with different styles by 27 speakers (14 Male and 13 female) for training and one long emotional speech by each speaker for testing. The experiments are performed for the cases of (i) text-independent but speaker-dependent and (ii) text-independent and speaker-independent.