Modern cars provide versatile tools to enhance speech communication. While an in-car communication (ICC) system aims at enhancing communication between the passengers by playing back desired speech via loudspeakers in the car, these loudspeaker signals may disturb a speech enhancement system required for hands-free telephony and automatic speech recognition. In this paper, we focus on speech zone detection, i.e. detecting which passenger in the car is speaking, which is a crucial component of the speech enhancement system. We propose a model-based feedback estimation method to improve robustness of speech zone detection against ICC feedback. Specifically, since the zone detection system typically does not have access to the ICC loudspeaker signals, the proposed method estimates the feedback signal from the observed microphone signals based on a free-field propagation model between the loudspeakers and the microphones as well as the ICC gain. We propose an efficient recursive implementation in the short-time Fourier transform domain using convolutive transfer functions. A realistic simulation study indicates that the proposed method allows to increase the ICC gain by about 6dB while still achieving robust speech zone detection results.
Electronics and Telecommunications Research Institute（电子与电信研究所）；MINDs Lab Inc.（MINDs实验室公司）
This paper introduces a large-scale Korean speech dataset, called VOTE400, that can be used for analyzing and recognizing voices of the elderly people. The dataset includes about 300 hours of continuous dialog speech and 100 hours of read speech, both recorded by the elderly people aged 65 years or over. A preliminary experiment showed that speech recognition system trained with VOTE400 can outperform conventional systems in speech recognition of elderly people's voice. This work is a multi-organizational effort led by ETRI and MINDs Lab Inc. for the purpose of advancing the speech recognition…
End-to-end automatic speech recognition systems have achieved great accuracy by using deeper and deeper models. However, the increased depth comes with a larger receptive field that can negatively impact model performance in streaming scenarios. We propose an alternative approach that we call Neural Mixture Model. The basic idea is to introduce a parallel mixture of shallow networks instead of a very deep network. To validate this idea we design CarneliNet -- a CTC-based neural network composed of three mega-blocks. Each mega-block consists of multiple parallel shallow sub-networks based on 1D…
ICMC-ASR: The ICASSP 2024 In-Car Multi-Channel Automatic Speech Recognition Challenge
Northwestern Polytechnical University（西北工业大学）；Space AI, Li Auto（理想汽车空间智能）；Beijing AI Shell Technology Co. Ltd（北京爱数智慧科技有限公司）；WeNet Open Source Community（WeNet开源社区）；ByteDance（字节跳动）；Microsoft Corporation（微软公司）；Tianjin University（天津大学）；Nanyang Technological University（南洋理工大学）；China Academy of Information and Communication Technology（中国信息通信研究院）
The two most common paradigms for end-to-end speech recognition are connectionist temporal classification (CTC) and attention-based encoder-decoder (AED) models. It has been argued that the latter is better suited for learning an implicit language model. We test this hypothesis by measuring temporal context sensitivity and evaluate how the models perform when we constrain the amount of contextual information in the audio input. We find that the AED model is indeed more context sensitive, but that the gap can be closed by adding self-attention to the CTC model. Furthermore, the two models perfo…
When evaluating the performance of automatic speech recognition models, usually word error rate within a certain dataset is used. Special care must be taken in understanding the dataset in order to report realistic performance numbers. We argue that many performance numbers reported probably underestimate the expected error rate. We conduct experiments controlling for selection bias, gender as well as overlap (between training and test data) in content, voices, and recording conditions. We find that content overlap has the biggest impact, but other factors like gender also play a role.
Fédération des Caisses Desjardins du Québec（魁北克德雅尔丹储蓄信贷联盟）
This paper introduces a new synthesis-based defense algorithm for counteracting with a varieties of adversarial attacks developed for challenging the performance of the cutting-edge speech-to-text transcription systems. Our algorithm implements a Sobolev-based GAN and proposes a novel regularizer for effectively controlling over the functionality of the entire generative model, particularly the discriminator network during training. Our achieved results upon carrying out numerous experiments on the victim DeepSpeech, Kaldi, and Lingvo speech transcription systems corroborate the remarkable per…
Speech rhythms have been dealt with in three main ways: from the introspective analyses of rhythm as a correlate of syllable and foot timing in linguistics and applied linguistics, through analyses of durations of segments of utterances associated with consonantal and vocalic properties, syllables, feet and words, to models of rhythms in speech production and perception as physical oscillations. The present study avoids introspection and human-filtered annotation methods and extends the signal processing paradigm of amplitude envelope spectrum analysis by adding an additional analytic step of …
Institute of Smart Systems and Artificial Intelligence (ISSAI)（智能系统与人工智能研究所（ISSAI））；Nazarbayev University（纳扎尔巴耶夫大学）
We present an open-source speech corpus for the Kazakh language. The Kazakh speech corpus (KSC) contains around 332 hours of transcribed audio comprising over 153,000 utterances spoken by participants from different regions and age groups, as well as both genders. It was carefully inspected by native Kazakh speakers to ensure high quality. The KSC is the largest publicly available database developed to advance various Kazakh speech and language processing applications. In this paper, we first describe the data collection and preprocessing procedures followed by a description of the database sp…
In this paper, the Lingban entry to the third 'CHiME' speech separation and recognition challenge is presented. A time-frequency masking based speech enhancement front-end is proposed to suppress the environmental noise utilizing multi-channel coherence and spatial cues. The state-of-the-art speech recognition techniques, namely recurrent neural network based acoustic and language modeling, state space minimum Bayes risk based discriminative acoustic modeling, and i-vector based acoustic condition modeling, are carefully integrated into the speech recognition back-end. To further improve the s…
Tsinghua University（清华大学）；Research Institute of Information Technology（信息技术研究院）；Center for Speech and Language Technology（语音与语言技术中心）
Speech data is crucially important for speech recognition research. There are quite some speech databases that can be purchased at prices that are reasonable for most research institutes. However, for young people who just start research activities or those who just gain initial interest in this direction, the cost for data is still an annoying barrier. We support the `free data' movement in speech recognition: research institutes (particularly supported by public funds) publish their data freely so that new researchers can obtain sufficient data to kick of their career. In this paper, we foll…
Bielefeld University（比勒费尔德大学）；Guangdong University of Finance（广东财经大学）
The objective of the present study is exploratory: to introduce and apply a new theory of speech rhythm zones or rhythm formants (R-formants). R-formants are zones of high magnitude frequencies in the low frequency (LF) long-term spectrum (LTS), rather like formants in the short-term spectra of vowels and consonants. After an illustration of the method, an R-formant analysis is made of non-elicited extracts from public speeches. The LF-LTS of three domains, the amplitude modulated (AM) absolute (rectified) signal, the amplitude envelope modulation (AEM) and frequency modulation (FM, F0, 'pitch…
In this paper we investigate the task of detecting carrier frequency differences from demodulated single sideband signals by examining the pitch contours of the received baseband speech signal in the short-time spectral domain. From the detected pitch frequency trajectory and its harmonics a carrier frequency difference, which is caused by demodulating the radio signal with the wrong carrier frequency, can be deduced. A computationally efficient realization in the power cepstral domain is presented. The core component, i.e., the pitch tracking algorithm, is shown to perform comparably to a sta…
Center for Artificial Intelligence Research (CAiRE)（人工智能研究中心（CAiRE））；The Hong Kong University of Science and Technology（香港科技大学）
With the rise of deep learning and intelligent vehicles, the smart assistant has become an essential in-car component to facilitate driving and provide extra functionalities. In-car smart assistants should be able to process general as well as car-related commands and perform corresponding actions, which eases driving and improves safety. However, in this research field, most datasets are in major languages, such as English and Chinese. There is a huge data scarcity issue for low-resource languages, hindering the development of research and applications for broader communities. Therefore, it i…
While current deep learning (DL)-based beamforming techniques have been proved effective in speech separation, they are often designed to process narrow-band (NB) frequencies independently which results in higher computational costs and inference times, making them unsuitable for real-world use. In this paper, we propose DL-based mel-subband spatio-temporal beamformer to perform speech separation in a car environment with reduced computation cost and inference time. As opposed to conventional subband (SB) approaches, our framework uses a mel-scale based subband selection strategy which ensures…
Most neural network speech enhancement models ignore speech production mathematical models by directly mapping Fourier transform spectrums or waveforms. In this work, we propose a neural source filter network for speech enhancement. Specifically, we use homomorphic signal processing and cepstral analysis to obtain noisy speech's excitation and vocal tract. Unlike traditional signal processing, we use an attentive recurrent network (ARN) model predicted ratio mask to replace the liftering separation function. Then two convolutional attentive recurrent network (CARN) networks are used to predict…
Speech enhancement with frequency domain auto-regressive modeling
Indian Institute of Science（印度科学学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Johns Hopkins University（约翰斯·霍普金斯大学）；Samsung Research India（三星印度研究院）
