Recent literature has shown that a learned front end with multi-channel audio input can outperform traditional beam-forming algorithms for automatic speech recognition (ASR). In this paper, we present our study on multi-channel acoustic modeling using OPUS compression with different bitrates for the different channels. We analyze the degradation in word error rate (WER) as a function of the audio encoding bitrate and show that the WER degrades by 12.6% relative with 16kpbs as compared to uncompressed audio. We show that its always preferable to have a multi-channel audio input over a single channel audio input given limited bandwidth. Our results show that for the best WER, when one of the two channels can be encoded with a bitrate higher than 32kbps, its optimal to encode the other channel with the highest bitrate possible. For bitrates lower than that, its preferable to distribute the bitrate equally between the two channels. We further show that by training the acoustic model on mixed bitrate input, up to 50% of the degradation can be recovered using a single model.
Automatic speech recognition (ASR) in the cloud allows the use of larger models and more powerful multi-channel signal processing front-ends compared to on-device processing. However, it also adds an inherent latency due to the transmission of the audio signal, especially when transmitting multiple channels of a microphone array. One way to reduce the network bandwidth requirements is client-side compression with a lossy codec such as Opus. However, this compression can have a detrimental effect especially on multi-channel ASR front-ends, due to the distortion and loss of spatial information i…
Analysis of Speaker Verification Performance Trade-offs with Neural Audio Codec Transmission
Indian Institute of Science Education and Research, Bhopal（印度科学教育与研究学院，博帕尔）；Nanyang Technological University, Singapore（南洋理工大学，新加坡）；Menlo Research（Menlo研究）
Dolby Laboratories, Inc.（杜比实验室公司）；Dolby Sweden AB（杜比瑞典公司）
We propose a neural audio generative model, MDCTNet, operating in the perceptually weighted domain of an adaptive modified discrete cosine transform (MDCT). The architecture of the model captures correlations in both time and frequency directions with recurrent layers (RNNs). An audio coding system is obtained by training MDCTNet on a diverse set of fullband monophonic audio signals at 48 kHz sampling, conditioned by a perceptual audio encoder. In a subjective listening test with ten excerpts chosen to be balanced across content types, yet stressful for both codecs, the mean performance of the…
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China（清华大学深圳国际研究生院）；Tencent AI Lab（腾讯AI实验室）；The Chinese University of Hong Kong, Hong Kong SAR, China（香港中文大学）
Communication University of China（中国传媒大学）；Microsoft Research Asia（微软亚洲研究院）
Bitrate scalability is a desirable feature for audio coding in real-time communications. Existing neural audio codecs usually enforce a specific bitrate during training, so different models need to be trained for each target bitrate, which increases the memory footprint at the sender and the receiver side and transcoding is often needed to support multiple receivers. In this paper, we introduce a cross-scale scalable vector quantization scheme (CSVQ), in which multi-scale features are encoded progressively with stepwise feature fusion and refinement. In this way, a coarse-level signal is recon…
Speaking Clearly: A Simplified Whisper-Based Codec for Low-Bitrate Speech Coding
School of Computer Science and Artificial Intelligence（计算机科学与人工智能学院）；Wuhan University of Technology（武汉理工大学）；NEC Corporation（日本电气株式会社）；The Hong Kong Polytechnic University（香港理工大学）
Universal Speech Token Learning via Low-Bitrate Neural Codec and Pretrained Representations
School of Information and Communication Engineering, Communication University of China（信息与通信工程学院，中国传媒大学）；Microsoft Research Asia（微软亚洲研究院）；State Key Laboratory of Media Convergence and Communication, Communication University of China（媒体融合与通信国家重点实验室，中国传媒大学）
We present SoundStream, a novel neural audio codec that can efficiently compress speech, music and general audio at bitrates normally targeted by speech-tailored codecs. SoundStream relies on a model architecture composed by a fully convolutional encoder/decoder network and a residual vector quantizer, which are trained jointly end-to-end. Training leverages recent advances in text-to-speech and speech enhancement, which combine adversarial and reconstruction losses to allow the generation of high-quality audio content from quantized embeddings. By training with structured dropout applied to q…
FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks
Concordia University（康科德大学）；Mila-Quebec AI Institute（魁北克AI研究院）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；University of Trento（特伦托大学）；Université Laval（拉瓦尔大学）
Collaboration AI, Cisco Systems, Inc.（Collaboration AI，Cisco系统公司）；Hebrew University of Jerusalem, Israel（耶路撒冷希伯来大学）；University of Illinois Urbana-Champaign, United States（伊利诺伊大学厄巴纳-香槟分校）；Carnegie Mellon University, United States（卡内基梅隆大学）
University of Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）；International Audio Laboratories（国际音频实验室）
Previous DCASE challenges contributed to an increase in the performance of acoustic scene classification systems. State-of-the-art classifiers demand significant processing capabilities and memory which is challenging for resource-constrained mobile or IoT edge devices. Thus, it is more likely to deploy these models on more powerful hardware and classify audio recordings previously uploaded (or streamed) from low-power edge devices. In such scenario, the edge device may apply perceptual audio coding to reduce the transmission data rate. This paper explores the effect of perceptual audio coding…
Voice-driven services (VDS) are being used in a variety of applications ranging from smart home control to payments using digital assistants. The input to such services is often captured via an open voice channel, e.g., using a microphone, in an unsupervised setting. One of the key operational security requirements in such setting is the freshness of the input speech. We present AEOLUS, a security overlay that proactively embeds a dynamic acoustic nonce at the time of user interaction, and detects the presence of the embedded nonce in the recorded speech to ensure freshness. We demonstrate tha…
