In speaker identification, most of the computation originates from distance or likelihood computations between the feature vectors of the unknown speaker and the models in the database. The identification time depends on the number of feature vectors, their dimensionality, the complexity of the speaker models and the number of speakers. In this paper, we focus on optimizing vector quantization (VQ) based speaker identification. We reduce the number of test vectors by pre-quantizing the test sequence prior to matching, and the number of speakers by pruning out unlikely speakers during the identification process. The best variants are then generalized to Gaussian mixture model (GMM) based modeling also. We obtain a speed-up factor of 16:1 with VQ-based system, and 34:1 with GMM-based system with a minor degradation in the identification error rate.

/pdf/real-time-speaker-identification-3s35nh2ydz.pdf

Real-time speaker identification.

Each person’s voice is different. Thus, the Quran sound, which had been recited by most of recitors will probably tend to differ a lot from one person to another. Although those Quranic sentence were particularly taken from the same verse, but the way of the sentence in Al-Quran been recited or delivered may be different. It may produce the difference sounds for the different recitors. Those same combinations of letters may be pronounced differently due to the use of harakates. This paper explores the viability of Mel-Frequency Cepstral Coefficient (MFCC) technique to extract features from Quranic verse recitation. Features extraction is crucial to prepare data for classification process. MFCC is one of the most popular feature extraction techniques used in speech recognition, whereby it is based on the frequency domain of Mel scale for human ear scale. MFCCs consist of preprocessing, framing, windowing, DFT, Mel Filterbank, Logarithm and Inverse DFT.

Quranic Verse Recitation Feature Extraction using Mel-Frequency Cepstral Coefficient (MFCC)

The Formosa speech database (ForSDat) is a multilingual speech corpus collected at Chang Gung University and sponsored by the National Science Council of Taiwan. It is expected that a multilingual speech corpus will be collected, covering the three most frequently used languages in Taiwan: Taiwanese (Min-nan), Hakka, and Mandarin. This 3-year project has the goal of collecting a phonetically abundant speech corpus of more than 1,800 speakers and hundreds of hours of speech. Recently, the first version of this corpus containing speech of 600 speakers of Taiwanese and Mandarin was finished and is ready to be released. It contains about 49 hours of speech and 247,000 utterances.

/pdf/toward-constructing-a-multilingual-speech-corpus-for-1nlnrf4sqs.pdf

Toward Constructing A Multilingual Speech Corpus for Taiwanese (Min-nan), Hakka, and Mandarin

This paper presents an effective method for improving the performance of a speaker identification system. Based on the multiresolution property of the wavelet transform, the input speech signal is decomposed into various frequency bands in order not to spread noise distortions over the entire feature space. To capture the characteristics of the vocal tract, the linear predictive cepstral coefficients (LPCCs) of each band are calculated. Furthermore, the cepstral mean normalization technique is applied to all computed features in order to provide similar parameter statistics in all acoustic environments. In order to effectively utilize these multiband speech features, we use feature recombination and likelihood recombination methods to evaluate the task of text-independent speaker identification. The feature recombination scheme combines the cepstral coefficients of each band to form a single feature vector used to train the Gaussian mixture model (GMM). The likelihood recombination scheme combines the likelihood scores of the independent GMM for each band. Experimental results show that both proposed methods achieve better performance than GMM using full-band LPCCs and mel-frequency cepstral coefficients (MFCCs) when the speaker identification is evaluated in the presence of clean and noisy environments.

/pdf/multiband-approach-to-robust-text-independent-speaker-1bh4odq3sg.pdf

Multiband Approach to Robust Text-Independent Speaker Identification

We present a novel approach for automatically learning models of temporal trajectories extracted from video data Instead of using a representation of linearly time-normalised vectors of fixed-length, our approach makes use of Dynamic Time Warp distance as a similarity measure to capture the underlying ordered structure of variable-length temporal data while removing the non-linear warping of the time scale We reformulate the structure learning problem as an optimal graphpartitioning of the dataset to solely exploit Dynamic Time Warp similarity weights without the need for intermediate cluster centroid representations We extend the graph partitioning method and in particular, the Normalised Cut model originally introduced for static image segmentation to unsupervised clustering of temporal trajectories withfully automated model order selection By computing hierarchical average Dynamic Time Warp for eachcluster, we learn warp-free trajectory models and recover the time warp profiles and structural variance in the data We demonstrate the approach on modelling trajectories of continuous hand-gestures and moving objects in an indoor environment

/pdf/learning-intrinsic-video-content-using-levenshtein-distance-4d789fno90.pdf

Learning Intrinsic Video Content Using Levenshtein Distance in Graph Partitioning

The author reviews major methods of applying the vector quantization (VQ) technique to speech and speaker recognition. These include speech recognition based on the combination of VQ and the DTW/HMM (dynamic time warping/hidden Markov model) technique. VQ-distortion-based recognition, learning VQ algorithms, speaker adaptation by VQ-codebook mapping, and VQ-distortion-based speaker recognition. It is concluded that not only has the VQ technique reduced the amount of computation and storage, but it has also created new ideas for solving various problems in speech/speaker recognition. >

/pdf/vector-quantization-based-speech-recognition-and-speaker-ou3k0tp30k.pdf

S. Furui

Papers

Vector-quantization-based speech recognition and speaker recognition techniques