49 research outputs found
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
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
Speech enhancement and speech separation are two related tasks, whose purpose
is to extract either one or more target speech signals, respectively, from a
mixture of sounds generated by several sources. Traditionally, these tasks have
been tackled using signal processing and machine learning techniques applied to
the available acoustic signals. Since the visual aspect of speech is
essentially unaffected by the acoustic environment, visual information from the
target speakers, such as lip movements and facial expressions, has also been
used for speech enhancement and speech separation systems. In order to
efficiently fuse acoustic and visual information, researchers have exploited
the flexibility of data-driven approaches, specifically deep learning,
achieving strong performance. The ceaseless proposal of a large number of
techniques to extract features and fuse multimodal information has highlighted
the need for an overview that comprehensively describes and discusses
audio-visual speech enhancement and separation based on deep learning. In this
paper, we provide a systematic survey of this research topic, focusing on the
main elements that characterise the systems in the literature: acoustic
features; visual features; deep learning methods; fusion techniques; training
targets and objective functions. In addition, we review deep-learning-based
methods for speech reconstruction from silent videos and audio-visual sound
source separation for non-speech signals, since these methods can be more or
less directly applied to audio-visual speech enhancement and separation.
Finally, we survey commonly employed audio-visual speech datasets, given their
central role in the development of data-driven approaches, and evaluation
methods, because they are generally used to compare different systems and
determine their performance
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End-to-end Speech Separation with Neural Networks
Speech separation has long been an active research topic in the signal processing community with its importance in a wide range of applications such as hearable devices and telecommunication systems. It not only serves as a fundamental problem for all higher-level speech processing tasks such as automatic speech recognition, natural language understanding, and smart personal assistants, but also plays an important role in smart earphones and augmented and virtual reality devices.
With the recent progress in deep neural networks, the separation performance has been significantly advanced by various new problem definitions and model architectures. The most widely-used approach in the past years performs separation in time-frequency domain, where a spectrogram or a time-frequency representation is first calculated from the mixture signal and multiple time-frequency masks are then estimated for the target sources. The masks are applied on the mixture's time-frequency representation to extract the target representations, and then operations such as inverse short-time Fourier transform is utilized to convert them back to waveforms. However, such frequency-domain methods may have difficulties in modeling the phase spectrogram as the conventional time-frequency masks often only consider the magnitude spectrogram. Moreover, the training objectives for the frequency-domain methods are typically also in frequency-domain, which may not be inline with widely-used time-domain evaluation metrics such as signal-to-noise ratio and signal-to-distortion ratio.
The problem formulation of time-domain, end-to-end speech separation naturally arises to tackle the disadvantages in the frequency-domain systems. The end-to-end speech separation networks take the mixture waveform as input and directly estimate the waveforms of the target sources. Following the general pipeline of conventional frequency-domain systems which contains a waveform encoder, a separator, and a waveform decoder, time-domain systems can be design in a similar way while significantly improves the separation performance.
In this dissertation, I focus on multiple aspects in the general problem formulation of end-to-end separation networks including the system designs, model architectures, and training objectives. I start with a single-channel pipeline, which we refer to as the time-domain audio separation network (TasNet), to validate the advantage of end-to-end separation comparing with the conventional time-frequency domain pipelines. I then move to the multi-channel scenario and introduce the filter-and-sum network (FaSNet) for both fixed-geometry and ad-hoc geometry microphone arrays.
Next I introduce methods for lightweight network architecture design that allows the models to maintain the separation performance while using only as small as 2.5% model size and 17.6% model complexity. After that, I look into the training objective functions for end-to-end speech separation and describe two training objectives for separating varying numbers of sources and improving the robustness under reverberant environments, respectively. Finally I take a step back and revisit several problem formulations in end-to-end separation pipeline and raise more questions in this framework to be further analyzed and investigated in future works
WAKE WORD DETECTION AND ITS APPLICATIONS
Always-on spoken language interfaces, e.g. personal digital assistants, rely on a wake word to start processing spoken input. Novel methods are proposed to train a wake word detection system from partially labeled training data, and to use it in on-line applications. In the system, the prerequisite of frame-level alignment is removed, permitting the use of un-transcribed training examples that are annotated only for the presence/absence of the wake word. Also, an FST-based decoder is presented to perform online detection. The suite of methods greatly improve the wake word detection performance across several datasets.
A novel neural network for acoustic modeling in wake word detection is also investigated. Specifically, the performance of several variants of chunk-wise streaming Transformers tailored for wake word detection is explored, including looking-ahead to the next chunk, gradient stopping, different positional embedding methods and adding same-layer dependency between chunks. Experiments demonstrate that the proposed Transformer model outperforms the baseline convolutional network significantly with a comparable model size, while still maintaining linear complexity w.r.t. the input length.
For the application of the detected wake word in ASR, the problem of improving speech recognition with the help of the detected wake word is investigated. Voice-controlled house-hold devices face the difficulty of performing speech recognition of device-directed speech in the presence of interfering background speech. Two end-to-end models are proposed to tackle this problem with information extracted from the anchored segment. The anchored segment refers to the wake word segment of the audio stream, which contains valuable speaker information that can be used to suppress interfering speech and background noise. A multi-task learning setup is also explored where the ideal mask, obtained from a data synthesis procedure, is used to guide the model training. In addition, a way to synthesize "noisy" speech from "clean" speech is also proposed to mitigate the mismatch between training and test data. The proposed methods show large word error reduction for Amazon Alexa live data with interfering background speech, without sacrificing the performance on clean speech