Soft margin estimation for automatic speech recognition

In this study, a new discriminative learning framework, called soft margin estimation (SME), is proposed for estimating the parameters of continuous density hidden Markov models (HMMs). The proposed method makes direct use of the successful ideas of margin in support vector machines to improve generalization capability and decision feedback learning in discriminative training to enhance model separation in classifier design. SME directly maximizes the separation of competing models to enhance the testing samples to approach a correct decision if the deviation from training samples is within a safe margin. Frame and utterance selections are integrated into a unified framework to select the training utterances and frames critical for discriminating competing models. SME offers a flexible and rigorous framework to facilitate the incorporation of new margin-based optimization criteria into HMMs training. The choice of various loss functions is illustrated and different kinds of separation measures are defined under a unified SME framework. SME is also shown to be able to jointly optimize feature extraction and HMMs. Both the generalized probabilistic descent algorithm and the Extended Baum-Welch algorithm are applied to solve SME. SME has demonstrated its great advantage over other discriminative training methods in several speech recognition tasks. Tested on the TIDIGITS digit recognition task, the proposed SME approach achieves a string accuracy of 99.61%, the best result ever reported in literature. On the 5k-word Wall Street Journal task, SME reduced the word error rate (WER) from 5.06% of MLE models to 3.81%, with relative 25% WER reduction. This is the first attempt to show the effectiveness of margin-based acoustic modeling for large vocabulary continuous speech recognition in a HMMs framework. The generalization of SME was also well demonstrated on the Aurora 2 robust speech recognition task, with around 30% relative WER reduction from the clean-trained baseline.Ph.D.Committee Chair: Dr. Chin-Hui Lee; Committee Member: Dr. Anthony Joseph Yezzi; Committee Member: Dr. Biing-Hwang (Fred) Juang; Committee Member: Dr. Mark Clements; Committee Member: Dr. Ming Yua

Deep Learning Based Speech Enhancement and Its Application to Speech Recognition

Speech enhancement is the task that aims to improve the quality and the intelligibility of a speech signal that is degraded by ambient noise and room reverberation. Speech enhancement algorithms are used extensively in many audio- and communication systems, including mobile handsets, speech recognition, speaker verification systems and hearing aids. Recently, deep learning has achieved great success in many applications, such as computer vision, nature language processing and speech recognition. Speech enhancement methods have been introduced that use deep-learning techniques, as these techniques are capable of learning complex hierarchical functions using large-scale training data. This dissertation investigates the deep learning based speech enhancement and its application to robust Automatic Speech Recognition (ASR). We start our work by exploring generative adversarial network (GAN) based speech enhancement. We explore the techniques to extract information about the noise to aid in the reconstruction of the speech signals. The proposed framework, referred to as ForkGAN, is a novel general adversarial learning-based framework that combines deep-learning with conventional noise reduction techniques. We further extend ForkGAN to M-ForkGAN, which integrates feature mapping and mask learning into a unified framework using ForkGAN. Another variant of ForkGAN, named S-ForkGAN, operates on spectral-domain features, which could directly apply to ASR. Systematic evaluations demonstrate the effectiveness of the proposed approaches. Then, we propose a novel multi-stage learning speech enhancement system. Each stage comprises a self-attention (SA) block followed by stacks of temporal convolutional network (TCN) blocks with doubling dilation factors. Each stage generates a prediction that is refined in a subsequent stage. A fusion block is inserted at the input of later stages to re-inject original information. Moreover, we design several multi-scale architectures with perceptual loss. Experiments show that our proposed architectures can achieve the state of the art performance on several public datasets. Recently, modeling to learn the acoustic noisy-clean speech mapping has been enhanced by including auxiliary information such as visual cues, phonetic and linguistic information, and speaker information. We propose a novel speaker-aware speech enhancement (SASE) method that extracts speaker information from a clean reference using long short-term memory (LSTM) layers, and then uses a convolutional recurrent neural network (CRN) to embed the extracted speaker information. The SASE framework is extended with a self-attention mechanism. It is shown that a few seconds of clean reference speech is sufficient, and that the proposed SASE method performs well for a wide range of scenarios. Even though speech enhancement methods that are based on deep learning have demonstrated state-of-the-art performance when compared with conventional methodologies, current deep learning approaches heavily rely on supervised learning, which requires a large number of noisy- and clean-speech sample pairs for training. This is generally not practical in a realistic environment. One cannot simultaneously obtain both noisy and clean speech samples. Thus, most speech enhancement approaches are trained with simulated speech and clean targets. In addition, it would be hard to collect large-scale dataset for the low-resource languages. We propose a novel noise-to-noise speech enhancement (N2N-SE) method that addresses the parallel noisy-clean training data issue, we leverage signal reconstruction techniques by only using corrupted speech. The proposed N2N-SE framework includes a noise conversion module that is an auto-encoder that learns to mix noise with speech, and a speech enhancement module, that learns to reconstruct corrupted speech signals. In addition to additive noise, speech is also affected by reverberation, which is caused by the attenuated and delayed reflections of sound waves. These distortions, particularly when combined, can severely degrade speech intelligibility for human listeners and impact applications, e.g., automatic speech recognition (ASR) and speaker recognition. Thus, effective speech denoising and dereverberation will benefit both speech processing applications and human listeners. We investigate the deep-learning based approaches for both speech dereverberation and speech denoising using the cascade Conformer architecture. The experimental results show that the proposed cascade Conformer can be effective to suppress the noise and reverberation

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

Speech recognition based on phonetic features and acoustic landmarks

A probabilistic and statistical framework is presented for automatic speech recognition based on a phonetic feature representation of speech sounds. In this acoustic-phonetic approach, the speech recognition problem is hypothesized as a maximization of the joint posterior probability of a set of phonetic features and the corresponding acoustic landmarks. Binary classifiers of the manner phonetic features - syllabic, sonorant and continuant - are applied for the probabilistic detection of speech landmarks. The landmarks include stop bursts, vowel onsets, syllabic peaks, syllabic dips, fricative onsets and offsets, and sonorant consonant onsets and offsets. The classifiers use automatically extracted knowledge based acoustic parameters (APs) that are acoustic correlates of those phonetic features. For isolated word recognition with known and limited vocabulary, the landmark sequences are constrained using a manner class pronunciation graph. Probabilistic decisions on place and voicing phonetic features are then made using a separate set of APs extracted using the landmarks. The framework exploits two properties of the knowledge-based acoustic cues of phonetic features: (1) sufficiency of the acoustic cues of a phonetic feature for a decision on that feature and (2) invariance of the acoustic cues with respect to context. The probabilistic framework makes the acoustic-phonetic approach to speech recognition suitable for practical recognition tasks as well as compatible with probabilistic pronunciation and language models. Support vector machines (SVMs) are applied for the binary classification tasks because of their two favorable properties - good generalization and the ability to learn from a relatively small amount of high dimensional data. Performance comparable to Hidden Markov Model (HMM) based systems is obtained on landmark detection as well as isolated word recognition. Applications to rescoring of lattices from a large vocabulary continuous speech recognizer are also presented

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace