자연주의적 탐구조사(Naturalistic Inquiry)

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(08)61690-6.pdf

Closing the gap in a generation: health equity through action on the social determinants of health

We describe the design of Kaldi, a free, open-source toolkit for speech recognition research. Kaldi provides a speech recognition system based on finite-state automata (using the freely available OpenFst), together with detailed documentation and a comprehensive set of scripts for building complete recognition systems. Kaldi is written is C++, and the core library supports modeling of arbitrary phonetic-context sizes, acoustic modeling with subspace Gaussian mixture models (SGMM) as well as standard Gaussian mixture models, together with all commonly used linear and affine transforms. Kaldi is released under the Apache License v2.0, which is highly nonrestrictive, making it suitable for a wide community of users.

/pdf/the-kaldi-speech-recognition-toolkit-3g790z933t.pdf

The Kaldi Speech Recognition Toolkit

https://www.cs.swarthmore.edu/~turnbull/cs97/f09/paper/reynolds00.pdf

Speaker Verification Using Adapted Gaussian Mixture Models

This paper introduces and motivates the use of Gaussian mixture models (GMM) for robust text-independent speaker identification. The individual Gaussian components of a GMM are shown to represent some general speaker-dependent spectral shapes that are effective for modeling speaker identity. The focus of this work is on applications which require high identification rates using short utterance from unconstrained conversational speech and robustness to degradations produced by transmission over a telephone channel. A complete experimental evaluation of the Gaussian mixture speaker model is conducted on a 49 speaker, conversational telephone speech database. The experiments examine algorithmic issues (initialization, variance limiting, model order selection), spectral variability robustness techniques, large population performance, and comparisons to other speaker modeling techniques (uni-modal Gaussian, VQ codebook, tied Gaussian mixture, and radial basis functions). The Gaussian mixture speaker model attains 96.8% identification accuracy using 5 second clean speech utterances and 80.8% accuracy using 15 second telephone speech utterances with a 49 speaker population and is shown to outperform the other speaker modeling techniques on an identical 16 speaker telephone speech task. >

Robust text-independent speaker identification using Gaussian mixture speaker models

https://hal.archives-ouvertes.fr/hal-00499180/document

Automatic speech recognition and speech variability: A review

A speaker-independent hidden Markov model (HMM) keyword recognizer (KWR) based on a continuous-speech-recognition model is presented. The baseline keyword recognition system is described, and techniques for dealing with nonkeyword speech and linear channel effects are discussed. The training of acoustic models to provide an explicit representation of nonvocabulary speech is investigated. A likelihood ratio scoring procedure is used to account for sources of variability affecting keyword likelihood scores. An acoustic class-dependent spectral normalization procedure is used to provide explicit compensation for linear channel effects. Keyword recognition results for a standard conversational speech task with a 20-keyword vocabulary reach 82% probability of detection at a false alarm rate of 12 false alarms per keyword per hour. >

A hidden Markov model based keyword recognition system

In an effort to reduce the degradation in speech recognition performance caused by variation in vocal tract shape among speakers, a frequency warping approach to speaker normalization is investigated. A set of low complexity, maximum likelihood based frequency warping procedures have been applied to speaker normalization for a telephone based connected digit recognition task. This paper presents an efficient means for estimating a linear frequency warping factor and a simple mechanism for implementing frequency warping by modifying the filter-bank in mel-frequency cepstrum feature analysis. An experimental study comparing these techniques to other well-known techniques for reducing variability is described. The results showed that frequency warping was consistently able to reduce word error rate by 20% even for very short utterances.

Speaker normalization using efficient frequency warping procedures

In an effort to reduce the degradation in speech recognition performance caused by variation in vocal tract shape among speakers, a frequency warping approach to speaker normalization is investigated. A set of low complexity, maximum likelihood based frequency warping procedures have been applied to speaker normalization for a telephone based connected digit recognition task. This paper presents an efficient means for estimating a linear frequency warping factor and a simple mechanism for implementing frequency warping by modifying the filterbank in mel-frequency cepstrum feature analysis. An experimental study comparing these techniques to other well-known techniques for reducing variability is described. The results have shown that frequency warping is consistently able to reduce word error rate by 20% even for very short utterances.

/pdf/a-frequency-warping-approach-to-speaker-normalization-56s3hvnfo5.pdf

Richard Rose

Papers

Robust text-independent speaker identification using Gaussian mixture speaker models

Automatic speech recognition and speech variability: A review

A hidden Markov model based keyword recognition system

Speaker normalization using efficient frequency warping procedures

A frequency warping approach to speaker normalization