Probabilistic Self-Organizing Maps for Text-Independent Speaker Identification

The present paper introduces a novel speaker modeling technique for text-independent speaker identification using probabilistic self-organizing maps (PbSOMs). The basic motivation behind the introduced technique was to combine the self-organizing quality of the self-organizing maps and generative power of Gaussian mixture models. Experimental results show that the introduced modeling technique using probabilistic self-organizing maps significantly outperforms the traditional technique using the classical GMMs and the EM algorithm or its deterministic variant. More precisely, a relative accuracy improvement of roughly 39% has been gained, as well as, a much less sensitivity to the model-parameters initialization has been exhibited by using the introduced speaker modeling technique using probabilistic self-organizing maps

Phoneme Based Speaker Verification System Based on Two Stage Self-Organizing Map Design

Speaker verification is one of the pattern recognition task that authenticate a person by his or her voice. This thesis deals with a relatively new technique of classification that is the self-organizing map (SOM). Self-organizing map, as an unsupervised learning artificial neural network, rarely used as final classification step in pattern recognition task due to its relatively low accuracy. A two-stage self-organizing map design has been implemented in this thesis and showed improved results over conventional single stage design. For speech features extraction, this thesis does not introduce any new technique. A well study method that is the linear prediction analysis (LP A) has been used. Linear predictive analysis derived coefficients are extracted from segmented raw speech signal to train and test the front stage self-organizing map. Unlike other multistage or hierarchical self-organizing map designs, this thesis utilized residual vectors generated from front stage self-organizing map to train and test the second stage selforganizing map. The results showed that by breaking the classification tasks into two level or more detail resolution, an improvement of more than 5% can be obtained. Moreover, the computation time is also reduced greatly

Speaker segmentation and clustering

This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved

Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech

We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling changed

Biologically inspired speaker verification

Speaker verification is an active research problem that has been addressed using a variety of different classification techniques. However, in general, methods inspired by the human auditory system tend to show better verification performance than other methods. In this thesis three biologically inspired speaker verification algorithms are presented

An Empirical Evaluation of Zero Resource Acoustic Unit Discovery

Acoustic unit discovery (AUD) is a process of automatically identifying a categorical acoustic unit inventory from speech and producing corresponding acoustic unit tokenizations. AUD provides an important avenue for unsupervised acoustic model training in a zero resource setting where expert-provided linguistic knowledge and transcribed speech are unavailable. Therefore, to further facilitate zero-resource AUD process, in this paper, we demonstrate acoustic feature representations can be significantly improved by (i) performing linear discriminant analysis (LDA) in an unsupervised self-trained fashion, and (ii) leveraging resources of other languages through building a multilingual bottleneck (BN) feature extractor to give effective cross-lingual generalization. Moreover, we perform comprehensive evaluations of AUD efficacy on multiple downstream speech applications, and their correlated performance suggests that AUD evaluations are feasible using different alternative language resources when only a subset of these evaluation resources can be available in typical zero resource applications.Comment: 5 pages, 1 figure; Accepted for publication at ICASSP 201