Hierarchical multi-stream posterior based speech secognition system

Abstract. In this paper, we present initial results towards boosting posterior based speech recognition systems by estimating more informative posteriors using multiple streams of features and taking into account acoustic context (e.g., as available in the whole utterance), as well as possible prior information (such as topological constraints). These posteriors are estimated based on “state gamma posterior ” definition (typically used in standard HMMs training) extended to the case of multi-stream HMMs.This approach provides a new, principled, theoretical framework for hierarchical estimation/use of posteriors, multi-stream feature combination, and integrating appropriate context and prior knowledge in posterior estimates. In the present work, we used the resulting gamma posteriors as features for a standard HMM/GMM layer. On the OGI Digits database and on a reduced vocabulary version (1000 words) of the DARPA Conversational Telephone Speech-to-text (CTS) task, this resulted in significant performance improvement, compared to the stateof-the-art Tandem systems.

Enhanced Phone Posteriors for Improving Speech Recognition Systems

Using phone posterior probabilities has been increasingly explored for improving automatic speech recognition (ASR) systems. In this paper, we propose two approaches for hierarchically enhancing these phone posteriors, by integrating long acoustic context, as well as prior phonetic and lexical knowledge. In the first approach, phone posteriors estimated with a Multi-Layer Perceptron (MLP), are used as emission probabilities in HMM forward-backward recursions. This yields new enhanced posterior estimates integrating HMM topological constraints (encoding specific phonetic and lexical knowledge), and context. posteriors are post-processed by a secondary MLP, in order to learn inter and intra dependencies between the phone posteriors. These dependencies are prior phonetic knowledge. The learned knowledge is integrated in the posterior estimation during the inference (forward pass) of the second MLP, resulting in enhanced phone posteriors. We investigate the use of the enhanced posteriors in hybrid HMM/ANN and Tandem configurations. We propose using the enhanced posteriors as replacement, or as complementary evidences to the regular MLP posteriors. The proposed method has been tested on different small and large vocabulary databases, always resulting in consistent improvements in frame, phone and word recognition rates

Enhancing posterior based speech recognition systems

The use of local phoneme posterior probabilities has been increasingly explored for improving speech recognition systems. Hybrid hidden Markov model / artificial neural network (HMM/ANN) and Tandem are the most successful examples of such systems. In this thesis, we present a principled framework for enhancing the estimation of local posteriors, by integrating phonetic and lexical knowledge, as well as long contextual information. This framework allows for hierarchical estimation, integration and use of local posteriors from the phoneme up to the word level. We propose two approaches for enhancing the posteriors. In the first approach, phoneme posteriors estimated with an ANN (particularly multi-layer Perceptron – MLP) are used as emission probabilities in HMM forward-backward recursions. This yields new enhanced posterior estimates integrating HMM topological constraints (encoding specific phonetic and lexical knowledge), and long context. In the second approach, a temporal context of the regular MLP posteriors is post-processed by a secondary MLP, in order to learn inter and intra dependencies among the phoneme posteriors. The learned knowledge is integrated in the posterior estimation during the inference (forward pass) of the second MLP, resulting in enhanced posteriors. The use of resulting local enhanced posteriors is investigated in a wide range of posterior based speech recognition systems (e.g. Tandem and hybrid HMM/ANN), as a replacement or in combination with the regular MLP posteriors. The enhanced posteriors consistently outperform the regular posteriors in different applications over small and large vocabulary databases

Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification

There are a number of studies about extraction of bottleneck (BN) features from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases and triphone states for improving the performance of text-dependent speaker verification (TD-SV). However, a moderate success has been achieved. A recent study [1] presented a time contrastive learning (TCL) concept to explore the non-stationarity of brain signals for classification of brain states. Speech signals have similar non-stationarity property, and TCL further has the advantage of having no need for labeled data. We therefore present a TCL based BN feature extraction method. The method uniformly partitions each speech utterance in a training dataset into a predefined number of multi-frame segments. Each segment in an utterance corresponds to one class, and class labels are shared across utterances. DNNs are then trained to discriminate all speech frames among the classes to exploit the temporal structure of speech. In addition, we propose a segment-based unsupervised clustering algorithm to re-assign class labels to the segments. TD-SV experiments were conducted on the RedDots challenge database. The TCL-DNNs were trained using speech data of fixed pass-phrases that were excluded from the TD-SV evaluation set, so the learned features can be considered phrase-independent. We compare the performance of the proposed TCL bottleneck (BN) feature with those of short-time cepstral features and BN features extracted from DNNs discriminating speakers, pass-phrases, speaker+pass-phrase, as well as monophones whose labels and boundaries are generated by three different automatic speech recognition (ASR) systems. Experimental results show that the proposed TCL-BN outperforms cepstral features and speaker+pass-phrase discriminant BN features, and its performance is on par with those of ASR derived BN features. Moreover,....Comment: Copyright (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Multi-stream Processing for Noise Robust Speech Recognition

In this thesis, the framework of multi-stream combination has been explored to improve the noise robustness of automatic speech recognition (ASR) systems. The central idea of multi-stream ASR is to combine information from several sources to improve the performance of a system. The two important issues of multi-stream systems are which information sources (feature representations) to combine and what importance (weights) be given to each information source. In the framework of hybrid hidden Markov model/artificial neural network (HMM/ANN) and Tandem systems, several weighting strategies are investigated in this thesis to merge the posterior outputs of multi-layered perceptrons (MLPs) trained on different feature representations. The best results were obtained by inverse entropy weighting in which the posterior estimates at the output of the MLPs were weighted by their respective inverse output entropies. In the second part of this thesis, two feature representations have been investigated, namely pitch frequency and spectral entropy features. The pitch frequency feature is used along with perceptual linear prediction (PLP) features in a multi-stream framework. The second feature proposed in this thesis is estimated by applying an entropy function to the normalized spectrum to produce a measure which has been termed spectral entropy. The idea of the spectral entropy feature is extended to multi-band spectral entropy features by dividing the normalized full-band spectrum into sub-bands and estimating the spectral entropy of each sub-band. The proposed multi-band spectral entropy features were observed to be robust in high noise conditions. Subsequently, the idea of embedded training is extended to multi-stream HMM/ANN systems. To evaluate the maximum performance that can be achieved by frame-level weighting, we investigated an ``oracle test''. We also studied the relationship of oracle selection to inverse entropy weighting and proposed an alternative interpretation of the oracle test to analyze the complementarity of streams in multi-stream systems. The techniques investigated in this work gave a significant improvement in performance for clean as well as noisy test conditions

Crosslingual Tandem-SGMM: Exploiting Out-Of-Language Data for Acoustic Model and Feature Level Adaptation

Recent studies have shown that speech recognizers may benefit from data in languages other than the target language through efficient acoustic model- or feature-level adaptation. Crosslingual Tandem-Subspace Gaussian Mixture Models (SGMM) are successfully able to combine acoustic model- and feature-level adaptation techniques. More specifically, we focus on under-resourced languages (Afrikaans in our case) and perform feature-level adaptation through the estimation of phone class posterior features with a Multilayer Perceptron that was trained on data from a similar language with large amounts of available speech data (Dutch in our case). The same Dutch data can also be exploited on an acoustic model-level by training globally-shared SGMM parameters in a crosslingual way. The two adaptation techniques are indeed complementary and result in a crosslingual Tandem-SGMM system that yields relative improvement of about 22% compared to a standard speech recognizer on an Afrikaans phoneme recognition task. Interestingly, eventual score-level combination of the individual SGMM systems yields additional 3% relative improvement

Recent advances in the multi-stream HMM/ANN hybrid approach to noise robust ASR

In this article we review several successful extensions to the standard Hidden-Markov-Model/Artificial Neural Network (HMM/ANN) hybrid, which have recently made important contributions to the field of noise robust automatic speech recognition. The first extension to the standard hybrid was the ``multi-band hybrid'', in which a separate ANN is trained on each frequency subband, followed by some form of weighted combination of \ANN state posterior probability outputs prior to decoding. However, due to the inaccurate assumption of subband independence, this system usually gives degraded performance, except in the case of narrow-band noise. All of the systems which we review overcome this independence assumption and give improved performance in noise, while also improving or not significantly degrading performance with clean speech. The ``all-combinations multi-band'' hybrid trains a separate ANN for each subband combination. This, however, typically requires a large number of ANNs. The ``all-combinations multi-stream'' hybrid trains an ANN expert for every combination of just a small number of complementary data streams. Multiple ANN posteriors combination using maximum a-posteriori (MAP) weighting gives rise to the further successful strategy of hypothesis level combination by MAP selection. An alternative strategy for exploiting the classification capacity of ANNs is the ``tandem hybrid'' approach in which one or more ANN classifiers are trained with multi-condition data to generate discriminative and noise robust features for input to a standard ASR system. The ``multi-stream tandem hybrid'' trains an ANN for a number of complementary feature streams, permitting multi-stream data fusion. The ``narrow-band tandem hybrid'' trains an ANN for a number of particularly narrow frequency subbands. This gives improved robustness to noises not seen during training. Of the systems presented, all of the multi-stream systems provide generic models for multi-modal data fusion. Test results for each system are presented and discusse

Improving large vocabulary continuous speech recognition by combining GMM-based and reservoir-based acoustic modeling

In earlier work we have shown that good phoneme recognition is possible with a so-called reservoir, a special type of recurrent neural network. In this paper, different architectures based on Reservoir Computing (RC) for large vocabulary continuous speech recognition are investigated. Besides experiments with HMM hybrids, it is shown that a RC-HMM tandem can achieve the same recognition accuracy as a classical HMM, which is a promising result for such a fairly new paradigm. It is also demonstrated that a state-level combination of the scores of the tandem and the baseline HMM leads to a significant improvement over the baseline. A word error rate reduction of the order of 20\% relative is possible