30 accepted papers
Dereverberation has long been a crucial research topic in speech processing, aiming to alleviate the adverse effects of reverberation in voice communication and speech interaction systems. Among existing approaches, forward convolutional prediction (FCP) has recently attracted attention. It typicall…
Multi-channel target speaker extraction (MC-TSE) aims to extract a target speaker's voice from multi-speaker signals captured by multiple microphones. Existing methods often rely on auxiliary clues such as direction-of-arrival (DOA) or speaker embeddings. However, DOA-based approaches depend on expl…
In multi-channel speech enhancement and robust automatic speech recognition (ASR), beamforming can typically improve the signal-to-noise ratio (SNR) of the target speaker and produce reliable enhancement with little distortion to target speech. With this observation, we propose to leverage beamforme…
Source separation (SS) of acoustic signals is a research field that emerged in the mid-1990s and has flourished ever since. On the occasion of ICASSP’s 50<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> anniversary, we review the major contribut…
Blind Speech Separation (BSS) aims to separate multiple speech sources from audio mixtures recorded by a microphone array. The problem is challenging because it is a blind inverse problem, i.e., the microphone array geometry, the room impulse response (RIR), and the speech sources, are all unknown.…
While far-field multi-talker mixtures are recorded, each speaker can wear a close-talk microphone so that close-talk mixtures can be recorded at the same time. Although each close-talk mixture has a high signal-to-noise ratio (SNR) of the wearer, it has a very limited range of applications, as it al…
Previous Multimodal Information based Speech Processing (MISP) challenges mainly focused on audio-visual speech recognition (AVSR) with commendable success. However, the most advanced back-end recognition systems often hit performance limits due to the complex acoustic environments. This has prompte…
We propose FSB-LSTM, a novel long short-term memory (LSTM) based architecture that integrates full- and sub-band (FSB) modeling, for single- and multi-channel speech enhancement in the short-time Fourier transform (STFT) domain. The model maintains an information highway to flow an over-complete inp…
In this work we detail our submission to the Clarity ICASSP 2023 grand challenge, in which participants have to develop a strong target speech enhancement system for hearing-aid (HA) devices in noisy-reverberant environments. Our system builds on our previous submission at the Second Clarity Enhance…
We propose TF-GridNet, a novel multi-path deep neural network (DNN) operating in the time-frequency (T-F) domain, for monaural talker-independent speaker separation in anechoic conditions. The model stacks several multi-path blocks, each consisting of an intra-frame spectral module, a sub-band tempo…
In reverberant conditions with multiple concurrent speakers, each microphone acquires a mixture signal of multiple speakers at a different location. In over-determined conditions where the microphones out-number speakers, we can narrow down the solutions to speaker images and realize unsupervised sp…
Speech enhancement is a critical component of many user-oriented audio applications, yet current systems still suffer from distorted and unnatural outputs. While generative models have shown strong potential in speech synthesis, they are still lagging behind in speech enhancement. This work leverage…
This study investigates robust speaker localization for continuous speech separation and speaker diarization, where we use speaker directions to group non-contiguous segments of the same speaker. Assuming that speakers do not move and are located in different directions, the direction of arrival (DO…
Existing systems for sound event localization and detection (SELD) typically operate by estimating a source location for all classes at every time instant. In this paper, we propose an alternative class-conditioned SELD model for situations where we may not be interested in localizing all classes al…
The cocktail party problem aims at isolating any source of interest within a complex acoustic scene, and has long inspired audio source separation research. Recent efforts have mainly focused on separating speech from noise, speech from speech, musical instruments from each other, or sound events fr…
This paper describes our submission to the L3DAS22 Challenge Task 1, which consists of speech enhancement with 3D Ambisonic microphones. The core of our approach combines Deep Neural Network (DNN) driven complex spectral mapping with linear beamformers such as the multi-frame multi-channel Wiener fi…
This study leverages frame-wise speaker counting to switch between speech enhancement and speaker separation for continuous speaker separation. The proposed approach counts the number of speakers at each frame. If there is no speaker overlap, a speech enhancement model is used to suppress noise and…
This study proposes a multi-microphone complex spectral mapping approach for speech dereverberation on a fixed array geometry. In the proposed approach, a deep neural network (DNN) is trained to predict the real and imaginary (RI) components of direct sound from the stacked reverberant (and noisy) R…
This study investigates phase reconstruction for deep learning based monaural talker-independent speaker separation in the short-time Fourier transform (STFT) domain. The key observation is that, for a mixture oftwo sources, with their magnitudes accurately estimated and under a geometric constraint…
The recently proposed deep clustering framework represents a significant step towards solving the cocktail party problem. This study proposes and compares a variety of alternative objective functions for training deep clustering networks. In addition, whereas the original deep clustering work relied…
Deep learning based single-channel time-frequency (T-F) masking has shown considerable potential for beamforming and robust ASR. This paper proposes a simple but novel relative transfer function (RTF) estimation algorithm for microphone arrays, where the RTF between a reference signal and a non-refe…
The recently-proposed deep clustering algorithm represents a fundamental advance towards solving the cocktail party problem in the single-channel case. When multiple microphones are available, spatial information can be leveraged to differentiate signals from different directions. This study combine…
This study integrates complementary spectral and spatial information to elevate deep learning based time-frequency masking and acoustic beamforming. Coherence and directional features are designed as additional input features for deep neural network training to remove diffuse noise and other directi…
We propose a speech enhancement algorithm based on single- and multi-microphone processing techniques. The core of the algorithm estimates a time-frequency mask which represents the target speech and use masking-based beamforming to enhance corrupted speech. Specifically, in single-microphone proces…
In daily listening environments, speech is commonly corrupted by room reverberation and background noise. These distortions are detrimental to speech intelligibility and quality, and also severely degrade the performance of automatic speech and speaker recognition systems. In this paper, we propose…
Accurately recognizing speaker emotion and age/gender from speech can provide better user experience for many spoken dialogue systems. In this study, we propose to use deep neural networks (DNNs) to encode each utterance into a fixed-length vector by pooling the activations of the last hidden layer…
Supervised speech separation algorithms seldom utilize output patterns. This study proposes a novel recurrent deep stacking approach for time-frequency masking based speech separation, where the output context is explicitly employed to improve the accuracy of mask estimation. The key idea is to inco…
Batch normalization is a standard technique for training deep neural networks. In batch normalization, the input of each hidden layer is first mean-variance normalized and then linearly transformed before applying non-linear activation functions. We propose a novel unsupervised speaker adaptation te…
Speech separation or enhancement algorithms seldom exploit information about phoneme identities. In this study, we propose a novel phoneme-specific speech separation method. Rather than training a single global model to enhance all the frames, we train a separate model for each phoneme to process it…
Robustness against noise is crucial for automatic speech recognition systems in real-world environments. In this paper, we propose a novel approach that performs robust ASR by directly recognizing ratio masks. In the proposed approach, a deep neural network (DNN) is first trained to estimate the ide…