Modern smart glasses leverage machine learning to offer real-time transcriptions, considerably enriching human communication experiences. However, such systems frequently encounter challenges related to environmental noises, leading to decreased speech recognition. To improve voice quality, this work investigates directional source separation using the multi-microphone array. We explore multiple beamformers to assist source separation by strengthening the directional properties of speech signals. In addition to relying on predetermined beamformers, we investigate neural beamforming in multi-channel source separation, demonstrating that automatic learning directional characteristics effectively improves separation quality. Furthermore, we investigate the training strategies for ASR when utilizing separated outputs. Our results suggest that jointly training a directional speech separation and ASR model achieves the best overall performance while balancing the wearer and conversation partner’s performance.

This paper presents a low-power 16-bit 125 MS/s pipeline analog-to-digital converter (ADC). A switched-capacitor dynamic bias technique is proposed to reduce power consumption while maintaining excellent performance. An enhanced bootstrapped switch is implemented to improve the linearity further. The pipeline ADC is fabricated in a 0.18μm mixed signal complementary metal–oxide–semiconductor (CMOS) process with an area of 1.8mm2. The implemented ADC is measured at 125 MS/s conversion rate under 1.8 V supply, and it achieves beyond 100 dB spur-free dynamic range (SFDR) and 75.9 dB signal-to-noise ratio (SNR) after foreground calibration for capacitor mismatch errors while the power consumption is 189mW resulting in 0.3 pJ/conv.-step FoM (Figure-of-Merit).

This paper studies audio-visual noise suppression for egocentric videos -where the speaker is not captured in the video. Instead, potential noise sources are visible on screen with the camera emulating the off-screen speaker’s view of the outside world. This setting is different from prior work in audio-visual speech enhancement that relies on lip and facial visuals. In this paper, we first demonstrate that egocentric visual information is helpful for noise suppression. We compare object recognition and action classification-based visual feature extractors and investigate methods to align audio and visual representations. Then, we examine different fusion strategies for the aligned features, and locations within the noise suppression model to incorporate visual information. Experiments demonstrate that visual features are most helpful when used to generate additive correction masks. Finally, in order to ensure that the visual features are discriminative with respect to different noise types, we introduce a multi-task learning framework that jointly optimizes audio-visual noise suppression and video-based acoustic event detection. This proposed multi-task framework outperforms the audio-only baseline on all metrics, including a 0.16 PESQ improvement. Extensive ablations reveal the improved performance of the proposed model with multiple active distractors, overall noise types, and across different SNRs.

This paper introduces a novel approach for extracting speaker embeddings from audio mixtures of multiple overlapping voices. This approach is based on a multi-task neural network. The network first extracts a latent feature for each direction. This feature is used for detecting sound sources as well as identifying speakers. In contrast to traditional approaches, the proposed method does not rely on explicit sound source separation. The neural network model learns from data to extract the most suitable features of the sounds at different directions. The experiments using audio recordings of overlapping sound sources show that the proposed approach outperforms a beamforming-based traditional method.

The IEEE Spoken Language Technology Workshop (SLT) 2021 Alpha-mini Speech Challenge (ASC) is intended to improve research on keyword spotting (KWS) and sound source location (SSL) on humanoid robots. Many publications report significant improvements in deep learning based KWS and SSL on open source datasets in recent years. For deep learning model training, it is necessary to expand the data coverage to improve the model robustness. Thus, simulating multi-channel noisy and reverberant data from single-channel speech, noise, echo and room impulsive response (RIR) is widely adopted. However, this approach may generate mismatch between simulated data and recorded data in real application scenarios, especially echo data. In this challenge, we open source a sizable speech, keyword, echo and noise corpus for promoting data-driven methods, particularly deep-learning approaches on KWS and SSL. We also choose Alpha-mini, a humanoid robot produced by UBTECH equipped with a built-in four-microphone array on its head, to record development and evaluation sets under the actual Alpha-mini robot application scenario, including environ-mental noise as well as echo and mechanical noise generated by the robot itself for model evaluation. Furthermore, we illustrate the rules, evaluation methods and baselines for re-searchers to quickly assess their achievements and optimize their models.

Deep neural networks have been successfully applied to sound direction-of-arrival estimation under challenging conditions. However, such a learning-based approach requires a large amount of labeled training data, which is difficult to acquire. To address this problem, we propose a novel approach for multi-speaker direction-of-arrival estimation with data augmentation and weakly-supervised domain adaptation. We generate source domain data with simulation, and collect real data annotated with the number of sound sources as the weak labels. The real data are further augmented by mixing single-source segments. Then, weakly-supervised domain adaptation is applied to models pre-trained on the simulated data. We define a loss function for the adaptation process which exploits the weak labels and the mixture component information in the augmented data. Experiments with real robot audio data show that our proposed approach achieves similar performance as if the fully-labeled real data are used. This paper suggests an effective development procedure for DOA estimation models applied to new types of microphone arrays with minimal data collection efforts.

In this paper, we introduce spatial attention for refining the information in multi-direction neural beamformer for far-field automatic speech recognition. Previous approaches of neural beamformers with multiple look directions, such as the factored complex linear projection, have shown promising results. However, the features extracted by such methods contain redundant information, as only the direction of the target speech is relevant. We propose using a spatial attention subnet to weigh the features from different directions, so that the subsequent acoustic model could focus on the most relevant features for the speech recognition. Our experimental results show that spatial attention achieves up to 9% relative word error rate improvement over methods without the attention.

This paper presents the MuMMER data set, a data set for human-robot interaction scenarios that is available for research purposes1. It comprises 1h 29 min of multimodal recordings of people interacting with the social robot Pepper in entertainment scenarios, such as quiz, chat, and route guidance. In the 33 clips (of 1 to 4 min long) recorded from the robot point of view, the participants are interacting with the robot in an unconstrained manner.The data set exhibits interesting features and difficulties, such as people leaving the field of view, robot moving (head rotation with embedded camera in the head), different illumination conditions. The data set contains color and depth videos from a Kinect v2, an Intel D435, and the video from Pepper.All the visual faces and the identities in the data set were manually annotated, making the identities consistent across time and clips. The goal of the data set is to evaluate perception algorithms in multi-party human/robot interaction, in particular the re-identification part when a track is lost, as this ability is crucial for keeping the dialog history. The data set can easily be extended with other types of annotations.We also present a benchmark on this data set that should serve as a baseline for future comparison. The baseline system, IHPER2(Idiap Human Perception system) is available for research and is evaluated on the MuMMER data set. We show that an identity precision and recall of ~80% and a MOTA score above 80% are obtained.

Despite the recent success of deep neural network-based approaches in sound source localization, these approaches suffer the limitations that the required annotation process is costly, and the mismatch between the training and test conditions undermines the performance. This paper addresses the question of how models trained with simulation can be exploited for multiple sound source localization in real scenarios by domain adaptation. In particular, two domain adaptation methods are investigated: weak supervision and domain-adversarial training. Our experiments show that the weak supervision with the knowledge of the number of sources can significantly improve the performance of an unadapted model. However, the domain-adversarial training does not yield significant improvement for this particular problem.

We propose to use neural networks for simultaneous detection and localization of multiple sound sources in human-robot interaction. In contrast to conventional signal processing techniques, neural network-based sound source localization methods require fewer strong assumptions about the environment. Previous neural network-based methods have been focusing on localizing a single sound source, which do not extend to multiple sources in terms of detection and localization. In this paper, we thus propose a likelihood-based encoding of the network output, which naturally allows the detection of an arbitrary number of sources. In addition, we investigate the use of sub-band cross-correlation information as features for better localization in sound mixtures, as well as three different network architectures based on different motivations. Experiments on real data recorded from a robot show that our proposed methods significantly outperform the popular spatial spectrum-based approaches.

We propose a novel multi-task neural network-based approach for joint sound source localization and speech/non-speech classification in noisy environments. The network takes raw short time Fourier transform as input and outputs the likelihood values for the two tasks, which are used for the simultaneous detection, localization and classification of an unknown number of overlapping sound sources, Tested with real recorded data, our method achieves significantly better performance in terms of speech/non-speech classification and localization of speech sources, compared to method that performs localization and classification separately. In addition, we demonstrate that incorporating the temporal context can further improve the performance.