Spoken command recognition for robotics

In this thesis, I investigate spoken command recognition technology for robotics. While high robustness is expected, the distant and noisy conditions in which the system has to operate make the task very challenging. Unlike commercial systems which all rely on a "wake-up" word to initiate the interaction, the pipeline proposed here directly detect and recognizes commands from the continuous audio stream. In order to keep the task manageable despite low-resource conditions, I propose to focus on a limited set of commands, thus trading off flexibility of the system against robustness. Domain and speaker adaptation strategies based on a multi-task regularization paradigm are first explored. More precisely, two different methods are proposed which rely on a tied loss function which penalizes the distance between the output of several networks. The first method considers each speaker or domain as a task. A canonical task-independent network is jointly trained with task-dependent models, allowing both types of networks to improve by learning from one another. While an improvement of 3.2% on the frame error rate (FER) of the task-independent network is obtained, this only partially carried over to the phone error rate (PER), with 1.5% of improvement. Similarly, a second method explored the parallel training of the canonical network with a privileged model having access to i-vectors. This method proved less effective with only 1.2% of improvement on the FER. In order to make the developed technology more accessible, I also investigated the use of a sequence-to-sequence (S2S) architecture for command classification. The use of an attention-based encoder-decoder model reduced the classification error by 40% relative to a strong convolutional neural network (CNN)-hidden Markov model (HMM) baseline, showing the relevance of S2S architectures in such context. In order to improve the flexibility of the trained system, I also explored strategies for few-shot learning, which allow to extend the set of commands with minimum requirements in terms of data. Retraining a model on the combination of original and new commands, I managed to achieve 40.5% of accuracy on the new commands with only 10 examples for each of them. This scores goes up to 81.5% of accuracy with a larger set of 100 examples per new command. An alternative strategy, based on model adaptation achieved even better scores, with 68.8% and 88.4% of accuracy with 10 and 100 examples respectively, while being faster to train. This high performance is obtained at the expense of the original categories though, on which the accuracy deteriorated. Those results are very promising as the methods allow to easily extend an existing S2S model with minimal resources. Finally, a full spoken command recognition system (named iCubrec) has been developed for the iCub platform. The pipeline relies on a voice activity detection (VAD) system to propose a fully hand-free experience. By segmenting only regions that are likely to contain commands, the VAD module also allows to reduce greatly the computational cost of the pipeline. Command candidates are then passed to the deep neural network (DNN)-HMM command recognition system for transcription. The VoCub dataset has been specifically gathered to train a DNN-based acoustic model for our task. Through multi-condition training with the CHiME4 dataset, an accuracy of 94.5% is reached on VoCub test set. A filler model, complemented by a rejection mechanism based on a confidence score, is finally added to the system to reject non-command speech in a live demonstration of the system

Improving bottleneck features for Vietnamese large vocabulary continuous speech recognition system using deep neural networks

In this paper, the pre-training method based on denoising auto-encoder is investigated and proved to be good models for initializing bottleneck networks of Vietnamese speech recognition system that result in better recognition performance compared to base bottleneck features reported previously. The experiments are carried out on the dataset containing speeches on Voice of Vietnam channel (VOV). The results show that the DBNF extraction for Vietnamese recognition decreases relative word error rate by 14 % and 39 % compared to the base bottleneck features and MFCC baseline, respectively

Deep Spoken Keyword Spotting:An Overview

Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams and has become a fast-growing technology thanks to the paradigm shift introduced by deep learning a few years ago. This has allowed the rapid embedding of deep KWS in a myriad of small electronic devices with different purposes like the activation of voice assistants. Prospects suggest a sustained growth in terms of social use of this technology. Thus, it is not surprising that deep KWS has become a hot research topic among speech scientists, who constantly look for KWS performance improvement and computational complexity reduction. This context motivates this paper, in which we conduct a literature review into deep spoken KWS to assist practitioners and researchers who are interested in this technology. Specifically, this overview has a comprehensive nature by covering a thorough analysis of deep KWS systems (which includes speech features, acoustic modeling and posterior handling), robustness methods, applications, datasets, evaluation metrics, performance of deep KWS systems and audio-visual KWS. The analysis performed in this paper allows us to identify a number of directions for future research, including directions adopted from automatic speech recognition research and directions that are unique to the problem of spoken KWS

Low latency modeling of temporal contexts for speech recognition

This thesis focuses on the development of neural network acoustic models for large vocabulary continuous speech recognition (LVCSR) to satisfy the design goals of low latency and low computational complexity. Low latency enables online speech recognition; and low computational complexity helps reduce the computational cost both during training and inference. Long span sequential dependencies and sequential distortions in the input vector sequence are a major challenge in acoustic modeling. Recurrent neural networks have been shown to effectively model these dependencies. Specifically, bidirectional long short term memory (BLSTM) networks, provide state-of-the-art performance across several LVCSR tasks. However the deployment of bidirectional models for online LVCSR is non-trivial due to their large latency; and unidirectional LSTM models are typically preferred. In this thesis we explore the use of hierarchical temporal convolution to model long span temporal dependencies. We propose a sub-sampled variant of these temporal convolution neural networks, termed time-delay neural networks (TDNNs). These sub-sampled TDNNs reduce the computation complexity by ~5x, compared to TDNNs, during frame randomized pre-training. These models are shown to be effective in modeling long-span temporal contexts, however there is a performance gap compared to (B)LSTMs. As recent advancements in acoustic model training have eliminated the need for frame randomized pre-training we modify the TDNN architecture to use higher sampling rates, as the increased computation can be amortized over the sequence. These variants of sub- sampled TDNNs provide performance superior to unidirectional LSTM networks, while also affording a lower real time factor (RTF) during inference. However we show that the BLSTM models outperform both the TDNN and LSTM models. We propose a hybrid architecture interleaving temporal convolution and LSTM layers which is shown to outperform the BLSTM models. Further we improve these BLSTM models by using higher frame rates at lower layers and show that the proposed TDNN- LSTM model performs similar to these superior BLSTM models, while reducing the overall latency to 200 ms. Finally we describe an online system for reverberation robust ASR, using the above described models in conjunction with other data augmentation techniques like reverberation simulation, which simulates far-field environments, and volume perturbation, which helps tackle volume variation even without gain normalization

Discriminative and adaptive training for robust speech recognition and understanding

Robust automatic speech recognition (ASR) and understanding (ASU) under various conditions remains to be a challenging problem even with the advances of deep learning. To achieve robust ASU, two discriminative training objectives are proposed for keyword spotting and topic classification: (1) To accurately recognize the semantically important keywords, the non-uniform error cost minimum classification error training of deep neural network (DNN) and bi-directional long short-term memory (BLSTM) acoustic models is proposed to minimize the recognition errors of only the keywords. (2) To compensate for the mismatched objectives of speech recognition and understanding, minimum semantic error cost training of the BLSTM acoustic model is proposed to generate semantically accurate lattices for topic classification. Further, to expand the application of the ASU system to various conditions, four adaptive training approaches are proposed to improve the robustness of the ASR under different conditions: (1) To suppress the effect of inter-speaker variability on speaker-independent DNN acoustic model, speaker-invariant training is proposed to learn a deep representation in the DNN that is both senone-discriminative and speaker-invariant through adversarial multi-task training (2) To achieve condition-robust unsupervised adaptation with parallel data, adversarial teacher-student learning is proposed to suppress multiple factors of condition variability in the procedure of knowledge transfer from a well-trained source domain LSTM acoustic model to the target domain. (3) To further improve the adversarial learning for unsupervised adaptation with unparallel data, domain separation networks are used to enhance the domain-invariance of the senone-discriminative deep representation by explicitly modeling the private component that is unique to each domain. (4) To achieve robust far-field ASR, an LSTM adaptive beamforming network is proposed to estimate the real-time beamforming filter coefficients to cope with non-stationary environmental noise and dynamic nature of source and microphones positions.Ph.D

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

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR