Reconfigurable Computing for Speech Recognition: Preliminary Findings

Continuous real-time speech recognition is a highly computationally-demanding task, but one which can take good advantage of a parallel processing system. To this end, we describe proposals for, and preliminary findings of, research in implementing in programmable logic the decoder part of a speech recognition system. Recognition via Viterbi decoding of Hidden Markov Models is outlined, along with details of current implementations, which aim to exploit properties of the algorithm that could make it well-suited for devices such as FPGAs. The question of how to deal with limited resources, by reconfiguration or otherwise, is also addressed

KL-Divergence Guided Two-Beam Viterbi Algorithm on Factorial HMMs

This thesis addresses the problem of the high computation complexity issue that arises when decoding hidden Markov models (HMMs) with a large number of states. A novel approach, the two-beam Viterbi, with an extra forward beam, for decoding HMMs is implemented on a system that uses factorial HMM to simultaneously recognize a pair of isolated digits on one audio channel. The two-beam Viterbi algorithm uses KL-divergence and hierarchical clustering to reduce the overall decoding complexity. This novel approach achieves 60% less computation compared to the baseline algorithm, the Viterbi beam search, while maintaining 82.5% recognition accuracy.Ope

Combining semantic and syntactic structure for language modeling

Structured language models for speech recognition have been shown to remedy the weaknesses of n-gram models. All current structured language models are, however, limited in that they do not take into account dependencies between non-headwords. We show that non-headword dependencies contribute to significantly improved word error rate, and that a data-oriented parsing model trained on semantically and syntactically annotated data can exploit these dependencies. This paper also contains the first DOP model trained by means of a maximum likelihood reestimation procedure, which solves some of the theoretical shortcomings of previous DOP models.Comment: 4 page

Speech Recognition in noisy environment using Deep Learning Neural Network

Recent researches in the field of automatic speaker recognition have shown that methods based on deep learning neural networks provide better performance than other statistical classifiers. On the other hand, these methods usually require adjustment of a significant number of parameters. The goal of this thesis is to show that selecting appropriate value of parameters can significantly improve speaker recognition performance of methods based on deep learning neural networks. The reported study introduces an approach to automatic speaker recognition based on deep neural networks and the stochastic gradient descent algorithm. It particularly focuses on three parameters of the stochastic gradient descent algorithm: the learning rate, and the hidden and input layer dropout rates. Additional attention was devoted to the research question of speaker recognition under noisy conditions. Thus, two experiments were conducted in the scope of this thesis. The first experiment was intended to demonstrate that the optimization of the observed parameters of the stochastic gradient descent algorithm can improve speaker recognition performance under no presence of noise. This experiment was conducted in two phases. In the first phase, the recognition rate is observed when the hidden layer dropout rate and the learning rate are varied, while the input layer dropout rate was constant. In the second phase of this experiment, the recognition rate is observed when the input layers dropout rate and learning rate are varied, while the hidden layer dropout rate was constant. The second experiment was intended to show that the optimization of the observed parameters of the stochastic gradient descent algorithm can improve speaker recognition performance even under noisy conditions. Thus, different noise levels were artificially applied on the original speech signal

Direct Acoustics-to-Word Models for English Conversational Speech Recognition

Recent work on end-to-end automatic speech recognition (ASR) has shown that the connectionist temporal classification (CTC) loss can be used to convert acoustics to phone or character sequences. Such systems are used with a dictionary and separately-trained Language Model (LM) to produce word sequences. However, they are not truly end-to-end in the sense of mapping acoustics directly to words without an intermediate phone representation. In this paper, we present the first results employing direct acoustics-to-word CTC models on two well-known public benchmark tasks: Switchboard and CallHome. These models do not require an LM or even a decoder at run-time and hence recognize speech with minimal complexity. However, due to the large number of word output units, CTC word models require orders of magnitude more data to train reliably compared to traditional systems. We present some techniques to mitigate this issue. Our CTC word model achieves a word error rate of 13.0%/18.8% on the Hub5-2000 Switchboard/CallHome test sets without any LM or decoder compared with 9.6%/16.0% for phone-based CTC with a 4-gram LM. We also present rescoring results on CTC word model lattices to quantify the performance benefits of a LM, and contrast the performance of word and phone CTC models.Comment: Submitted to Interspeech-201