Deep Learning Based Complex Spectral Mapping for Multi-Channel Speaker Separation and Speech Enhancement

尽管在基于深度学习的语音分离和自动语音识别方面取得了巨大的进展，但如何在房间混响和背景噪声存在的情况下分离并发说话人并识别他们的语音仍然是一个主要挑战。该项目将开发一种多通道复谱映射方法来进行多说话人说话人分离和语音增强，以提高在这种条件下的语音识别性能。该方法训练深度神经网络，从复数域的多通道输入中预测说话人的实部和虚部。在将重叠的说话人分离成同时流之后，将对说话人二元化执行顺序分组，这是将同一说话者的语音话语与其他说话者的话语和停顿的间隔分组的任务。提出的说话人二值化方法综合了空间和频谱说话人特征，通过多通道说话人定位和单通道说话人嵌入提取说话人特征。递归神经网络将被训练来执行分类，以达到说话人对分的目的，这可以处理会议中任意数量的说话人。建议的分离系统将使用开放的多声道说话人分离数据集进行评估，该数据集包含房间混响和背景噪声。该项目的结果有望显著提高连续说话人分离以及说话人二元化在恶劣声学环境中的性能，帮助缩小识别单个说话者语音和识别多个说话者语音之间的性能差距。该项目的总体目标是开发一个深度学习系统，该系统可以在对话或会议环境中连续分离单个说话人，并准确识别这些说话人的话语。在同时分组以独立于说话者的方式分离和增强重叠说话者的最新进展的基础上，该项目主要侧重于说话者二元化，其目的是在不同时间对同一说话者的讲话进行分组。为了实现说话人的二值化，采用基于深度学习的顺序分组方法，综合了说话人的空间特征和频谱特征。通过顺序组织，将同时流与先前分离的说话人流进行分组，形成顺序流，每个顺序流对应于同一说话人到当前时间的所有话语。将研究说话人的定位和分类，以使顺序分组能够在会议场景中创建新的顺序流并处理任意数量的说话人。通过增加空间维度，拟议的二元化办法解决了谁在何时何地发言的问题，大大扩大了谁在何时发言的传统范围。建议的分离系统将使用多通道说话人分离数据集进行评估，这些数据集包含记录的对话中高度重叠的语音，以及真实环境中存在的房间混响和背景噪声。在自动语音识别中，主要评估指标将是单词错误率。该奖项反映了NSF的法定使命，通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为是值得支持的。

Despite tremendous advances in deep learning based speech separation and automatic speech recognition, a major challenge remains how to separate concurrent speakers and recognize their speech in the presence of room reverberation and background noise. This project will develop a multi-channel complex spectral mapping approach to multi-talker speaker separation and speech enhancement in order to improve speech recognition performance in such conditions. The proposed approach trains deep neural networks to predict the real and imaginary parts of individual talkers from the multi-channel input in the complex domain. After overlapped speakers are separated into simultaneous streams, sequential grouping will be performed for speaker diarization, which is the task of grouping the speech utterances of the same talker over intervals with the utterances of other speakers and pauses. Proposed speaker diarization will integrate spatial and spectral speaker features, which will be extracted through multi-channel speaker localization and single-channel speaker embedding. Recurrent neural networks will be trained to perform classification for the purpose of speaker diarization, which can handle an arbitrary number of speakers in a meeting. The proposed separation system will be evaluated using open, multi-channel speaker separation datasets that contain both room reverberation and background noise. The results from this project are expected to substantially elevate the performance of continuous speaker separation, as well as speaker diarization, in adverse acoustic environments, helping to close the performance gap between recognizing single-talker speech and recognizing multi-talker speech.The overall goal of this project is to develop a deep learning system that can continuously separate individual speakers in a conversational or meeting setting and accurately recognize the utterances of these speakers. Building on recent advances on simultaneous grouping to separate and enhance overlapped speakers in a talker-independent fashion, the project is mainly focused on speaker diarization, which aims to group the speech utterances of the same speaker across time. To achieve speaker diarization, deep learning based sequential grouping will be performed and it will integrate spatial and spectral speaker characteristics. Through sequential organization, simultaneous streams will be grouped with earlier-separated speaker streams to form sequential streams, each of which corresponds to all the utterances of the same speaker up to the current time. Speaker localization and classification will be investigated to make sequential grouping capable of creating new sequential streams and handling an arbitrary number of speakers in a meeting scenario. With the added spatial dimension, the proposed diarization approach provides a solution to the question of who spoke when and where, significantly expanding the traditional scope of who spoke when. The proposed separation system will be evaluated using multi-channel speaker separation datasets that contain highly overlapped speech in recorded conversations, as well as room reverberation and background noise present in real environments. The main evaluation metric will be word error rate in automatic speech recognition. The performance of speaker diarization will be measured using diarization error rate.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.