Bayesian Speaker Adaptation Based on a New Hierarchical Probabilistic Model

In this paper, a new hierarchical Bayesian speaker adaptation method called HMAP is proposed that combines the advantages of three conventional algorithms, maximum a posteriori (MAP), maximum-likelihood linear regression (MLLR), and eigenvoice, resulting in excellent performance across a wide range of adaptation conditions. The new method efficiently utilizes intra-speaker and inter-speaker correlation information through modeling phone and speaker subspaces in a consistent hierarchical Bayesian way. The phone variations for a specific speaker are assumed to be located in a low-dimensional subspace. The phone coordinate, which is shared among different speakers, implicitly contains the intra-speaker correlation information. For a specific speaker, the phone variation, represented by speaker-dependent eigenphones, are concatenated into a supervector. The eigenphone supervector space is also a low dimensional speaker subspace, which contains inter-speaker correlation information. Using principal component analysis (PCA), a new hierarchical probabilistic model for the generation of the speech observations is obtained. Speaker adaptation based on the new hierarchical model is derived using the maximum a posteriori criterion in a top-down manner. Both batch adaptation and online adaptation schemes are proposed. With tuned parameters, the new method can handle varying amounts of adaptation data automatically and efficiently. Experimental results on a Mandarin Chinese continuous speech recognition task show good performance under all testing conditions

Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information

This thesis explores methods to rapidly bootstrap automatic speech recognition systems for languages, which lack resources for speech and language processing. We focus on finding approaches which allow using data from multiple languages to improve the performance for those languages on different levels, such as feature extraction, acoustic modeling and language modeling. Under application aspects, this thesis also includes research work on non-native and Code-Switching speech

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

On adaptive decision rules and decision parameter adaptation for automatic speech recognition

Recent advances in automatic speech recognition are accomplished by designing a plug-in maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training corpora. Maximum-likelihood point estimation is by far the most prevailing training method. However, due to the problems of unknown speech distributions, sparse training data, high spectral and temporal variabilities in speech, and possible mismatch between training and testing conditions, a dynamic training strategy is needed. To cope with the changing speakers and speaking conditions in real operational conditions for high-performance speech recognition, such paradigms incorporate a small amount of speaker and environment specific adaptation data into the training process. Bayesian adaptive learning is an optimal way to combine prior knowledge in an existing collection of general models with a new set of condition-specific adaptation data. In this paper, the mathematical framework for Bayesian adaptation of acoustic and language model parameters is first described. Maximum a posteriori point estimation is then developed for hidden Markov models and a number of useful parameters densities commonly used in automatic speech recognition and natural language processing.published_or_final_versio

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks

Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks

Millions of people around the world are diagnosed with neurological disorders like Parkinson’s, Cerebral Palsy or Amyotrophic Lateral Sclerosis. Due to the neurological damage as the disease progresses, the person suffering from the disease loses control of muscles, along with speech deterioration. Speech deterioration is due to neuro motor condition that limits manipulation of the articulators of the vocal tract, the condition collectively called as dysarthria. Even though dysarthric speech is grammatically and syntactically correct, it is difficult for humans to understand and for Automatic Speech Recognition (ASR) systems to decipher. With the emergence of deep learning, speech recognition systems have improved a lot compared to traditional speech recognition systems, which use sophisticated preprocessing techniques to extract speech features. In this digital era there are still many documents that are handwritten many of which need to be digitized. Offline handwriting recognition involves recognizing handwritten characters from images of handwritten text (i.e. scanned documents). This is an interesting task as it involves sequence learning with computer vision. The task is more difficult than Optical Character Recognition (OCR), because handwritten letters can be written in virtually infinite different styles. This thesis proposes exploiting deep learning techniques like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for offline handwriting recognition. For speech recognition, we compare traditional methods for speech recognition with recent deep learning methods. Also, we apply speaker adaptation methods both at feature level and at parameter level to improve recognition of dysarthric speech

I-vector estimation using informative priors for adaptation of deep neural networks

This is the author accepted manuscript. The final version is available from ISCA via http://www.isca-speech.org/archive/interspeech_2015/i15_2872.html Supporting data for this paper is available at the http://www.repository.cam.ac.uk/handle/1810/248387 data repository.I-vectors are a well-known low-dimensional representation of speaker space and are becoming increasingly popular in adaptation of state-of-the-art deep neural network (DNN) acoustic models. One advantage of i-vectors is that they can be used with very little data, for example a single utterance. However, to improve robustness of the i-vector estimates with limited data, a prior is often used. Traditionally, a standard normal prior is applied to i-vectors, which is nevertheless not well suited to the increased variability of short utterances. This paper proposes a more informative prior, derived from the training data. As well as aiming to reduce the non-Gaussian behaviour of the i-vector space, it allows prior information at different levels, for example gender, to be used. Experiments on a US English Broadcast News (BN) transcription task for speaker and utterance i-vector adaptation show that more informative priors reduce the sensitivity to the quantity of data used to estimate the i-vector. The best configuration for this task was utterance-level test i-vectors enhanced with informative priors which gave a 13% relative reduction in word error rate over the baseline (no i-vectors) and a 5% over utterance-level test i-vectors with standard prior.This work was supported by EPSRC Programme Grant EP/I031022/1 (Natural Speech Technology)

VTLN-Based Rapid Cross-Lingual Adaptation for Statistical Parametric Speech Synthesis

Cross-lingual speaker adaptation (CLSA) has emerged as a new challenge in statistical parametric speech syn- thesis, with specific application to speech-to-speech translation. Recent research has shown that reasonable speaker similarity can be achieved in CLSA using maximum likelihood linear transformation of model parameters, but this method also has weaknesses due to the inherent mismatch caused by differing phonetic inventories of languages. In this paper, we propose that fast and effective CLSA can be made using vocal tract length normalization (VTLN), where strong constraints of the vocal tract warping function may actually help to avoid the most severe effects of the aforementioned mismatch. VTLN has a single parameter that warps spectrum. Using shifted or adapted pitch, VTLN can still achieve reasonable speaker similarity. We present our approach, VTLN-based CLSA, and evaluation results that support our proposal under the limitation that the voice identity and speaking style of a target speaker don’t diverge too far from that of the average voice model