SpeechWave

语音识别在过去几年中取得了重大进展。在标准的大规模任务中，例如交换机（会话式电话语音）、MGB（多类型广播录音）和AMI（多方会议），错误率降低了一半以上。这些研究进展已经迅速转化为商业产品和服务：基于语音的应用程序和助手，如苹果的Siri、亚马逊的Alexa和b谷歌语音搜索，已经成为许多人日常生活的一部分。支撑这些系统提高准确性的是声学建模的进步，深度学习在该领域产生了显著的影响。然而，语音识别仍然非常脆弱：它已经成功地部署在特定的声学条件和任务领域-例如，智能手机上的语音搜索-并且在条件改变时严重退化。这是因为语音识别极易受到由多个声源引起的加性噪声和混响的影响。在这两种情况下，对人类语音识别的准确性基本上没有影响的声学条件可能会对最先进的自动系统的准确性产生灾难性的影响。造成这种脆性的一个原因是缺乏一个强大的声学稳健性模型。鲁棒性通常通过多条件训练来解决，其中训练集包括许多所需声学条件下的语音示例，通常通过以不同信噪比混合语音和噪声来构建。对于一组有限的声学条件，这些技术可以很好地工作，但它们效率低下，不能提供多个声源的模型，也不能分解变化的原因。例如，使用单个远程麦克风记录的AMI语料库测试集转录的最佳语音识别结果约为38%的单词错误率（对于非重叠语音），而人类听众的错误率约为5%。在过去的几年里，有几种方法试图解决这些问题：明确地学习分离多个来源；使用辅助特征的分解声学模型；并学习了多通道波束形成的频谱掩模。speech wave将寻求一种鲁棒语音识别的替代方法：开发直接从语音波形中学习的声学模型。直接在波形域中操作的动机源于语音信号的冗余很可能是人类语音识别鲁棒性的关键因素。当前的语音识别方法将非自适应信号处理组件从自适应声学模型中分离出来，这样做就失去了语音波形中存在的冗余性——通常是相位等信息。波形模型特别令人兴奋，因为它们结合了以前不同的信号处理和声学建模组件。在《语音波》中，我们将探索新的基于波形的卷积和循环网络，这些网络以一种分解的方式结合了语音增强和识别，以及基于核方法和稀疏信号处理和语音感知方面的最新研究进展的方法。我们的研究将在标准的大规模语音语料库上进行评估。此外，我们将参与和组织国际挑战，以评估语音识别技术的性能。我们还将在实践中验证我们的技术，在我们的项目合作伙伴BBC、Emotech、Quorate和SRI面临的语音识别挑战的背景下。

Speech recognition has made major advances in the past few years. Error rates have been reduced by more than half on standard large-scale tasks such as Switchboard (conversational telephone speech), MGB (multi-genre broadcast recordings), and AMI (multiparty meetings). These research advances have quickly translated into commercial products and services: speech-based applications and assistants such as such as Apple's Siri, Amazon's Alexa, and Google voice search have become part of daily life for many people. Underpinning the improved accuracy of these systems are advances in acoustic modelling, with deep learning having had an outstanding influence on the field.However, speech recognition is still very fragile: it has been successfully deployed in specific acoustic conditions and task domains - for instance, voice search on a smart phone - and degrades severely when the conditions change. This is because speech recognition is highly vulnerable to additive noise caused by multiple acoustic sources, and to reverberation. In both cases, acoustic conditions which have essentially no effect on the accuracy of human speech recognition can have a catastrophic impact on the accuracy of a state-of-the-art automatic system. A reason for such brittleness is the lack of a strong model for acoustic robustness. Robustness is usually addressed through multi-condition training, in which the training set comprises speech examples across the many required acoustic conditions, often constructed by mixing speech with noise at different signal-to-noise ratios. For a limited set of acoustic conditions these techniques can work well, but they are inefficient and do not offer a model of multiple acoustic sources, nor do they factorise the causes of variability. For instance, the best reported speech recognition results for transcription of the AMI corpus test set using single distant microphone recordings is about 38% word error rate (for non-overlapped speech), compared to about 5% error rate for human listeners. In the past few years there have been several approaches that have tried to address these problems: explicitly learning to separate multiple sources; factorised acoustic models using auxiliary features; and learned spectral masks for multi-channel beam-forming. SpeechWave will pursue an alternative approach to robust speech recognition: The development of acoustic models which learn directly from the speech waveform. The motivation to operate directly in the waveform domain arises from the insight that redundancy in speech signals is highly likely to be a key factor in the robustness of human speech recognition. Current approaches to speech recognition separate non-adaptive signal processing components from the adaptive acoustic model, and in so doing lose the redundancy - and, typically, information such as the phase - present in the speech waveform. Waveform models are particularly exciting as they combine the previously distinct signal processing and acoustic modelling components.In SpeechWave, we shall explore novel waveform-based convolutional and recurrent networks which combine speech enhancement and recognition in a factorised way, and approaches based on kernel methods and on recent research advances in sparse signal processing and speech perception. Our research will be evaluated on standard large-scale speech corpora. In addition we shall participate in, and organise, international challenges to assess the performance of speech recognition technologies. We shall also validate our technologies in practice, in the context of the speech recognition challenges faced by our project partners BBC, Emotech, Quorate, and SRI.