“EURASIP JOURNAL ON AUDIO, SPEECH, AND MUSIC PROCESSING”旨在汇集致力于各种音频信号处理理论和应用的研究人员、科学家和工程师，特别关注语音和音乐。 EURASIP 音频、语音和音乐处理期刊将是一本跨学科期刊，用于传播语音通信和音频处理的所有基本和应用方面。

The "Eurasip Journal on Audio, Speech, and Music Processing" aims to bring together researchers, scientists, and engineers dedicated to the theory and application of various audio signal processing, with a particular focus on speech and music. This journal will serve as an interdisciplinary platform for disseminating all fundamental and applied aspects of speech communication and audio processing.

《Eurasip Journal on Audio, Speech, and Music Processing》旨在汇聚致力于音频信号处理理论与应用的研究人员、科学家和工程师，特别关注语音和音乐领域。该期刊将作为一个跨学科的平台，传播语音通信和音频处理的基本理论及其应用。

Eurasip Journal on Audio, Speech, and Music Processing

Abstract This article presents the research work on improving speech recognition systems for the morphologically complex Malayalam language using subword tokens for language modeling. The speech recognition system is built using a deep neural network–hidden Markov model (DNN-HMM)-based automatic speech recognition (ASR). We propose a novel method, syllable-byte pair encoding (S-BPE), that combines linguistically informed syllable tokenization with the data-driven tokenization method of byte pair encoding (BPE). The proposed method ensures words are always segmented at valid pronunciation boundaries. On a text corpus that has been divided into tokens using the proposed method, we construct statistical n-gram language models and assess the modeling effectiveness in terms of both information-theoretic and corpus linguistic metrics. A comparative study of the proposed method with other data-driven (BPE, Morfessor, and Unigram), linguistic (Syllable), and baseline (Word) tokenization algorithms is also presented. Pronunciation lexicons of subword tokenized units are built with pronunciation described as graphemes. We develop ASR systems employing the subword tokenized language models and pronunciation lexicons. The resulting ASR models are comprehensively evaluated to answer the research questions regarding the impact of subword tokenization algorithms on language modeling complexity and on ASR performance. Our study highlights the strong performance of the hybrid S-BPE tokens, achieving a notable 10.6% word error rate (WER), which represents a substantial 16.8% improvement over the baseline word-level ASR system. The ablation study has revealed that the performance of S-BPE segmentation, which initially underperformed compared to syllable tokens with lower amounts of textual data for language modeling, exhibited steady improvement with the increase in LM training data. The extensive ablation study indicates that there is a limited advantage in raising the n-gram order of the language model beyond $$n=3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> . Such an increase results in considerable model size growth without significant improvements in WER. The implementation of the algorithm and all associated experiments are available under an open license, allowing for reproduction, adaptation, and reuse.

Kavya Manohar

Jayan A R

Rajeev Rajan

EURASIP Journal on Audio Speech and Music Processing

Improving speech recognition systems for the morphologically complex Malayalam language using subword tokens for language modeling

使用子词标记改进形态复杂的马拉雅拉姆语的语音识别系统

Transfer learning has been shown to be effective in enhancing speaker verification performance in low-resource conditions. However, the inclusion of additional datasets may cause domain mismatch. Additionally, mismatched data volume and model complexity during fine-tuning can degrade speaker verification performance. In this paper, we propose a domain-weighted allocation fine-tuning strategy that employs the Kernel Mean Matching (KMM) algorithm to adjust the distribution differences between the in-domain and out-of-domain datasets. It assigns weights to each sample in the source datasets and utilizes the maximum mean discrepancy (MMD) distance to measure the effectiveness of distribution adaptation. The domain-weighted allocation fine-tuning strategy (DWA-FT) effectively mitigates the issue of domain mismatch during model training. We also propose two backend canonical correlation analysis (CCA) embedding transformation methods, the CCA embedding fusion and the CCA embedding constraint. These methods aim to enhance the quality of speaker embeddings. The experimental results demonstrate that the proposed methods effectively enhance the performance of the speaker verification system in low-resource scenarios. Compared to the baseline, our methods achieve relative improvements of 51.03% in PLDA scoring and 46.02% in cosine similarity scoring on the Himia dataset.

Han Wang

Mingrui He

Mingjun Zhang

Changzhi Luo

Longting Xu

Domain-weighted transfer learning and discriminative embeddings for low-resource speaker verification

领域加权迁移学习与判别嵌入在低资源说话人验证中的应用

Abstract In this study, we investigate the effectiveness of spatial features in acoustic scene classification using distributed microphone arrays. Under the assumption that multiple subarrays, each equipped with microphones, are synchronized, we investigate two types of spatial feature: intra- and inter-generalized cross-correlation phase transforms (GCC-PHATs). These are derived from channels within the same subarray and between different subarrays, respectively. Our approach treats the log-Mel spectrogram as a spectral feature and intra- and/or inter-GCC-PHAT as a spatial feature. We propose two integration methods for spectral and spatial features: (a) middle integration, which fuses embeddings obtained by spectral and spatial features, and (b) late integration, which fuses decisions estimated using spectral and spatial features. The evaluation experiments showed that, when using only spectral features, employing all channels did not markedly improve the F1-score compared with the single-channel case. In contrast, integrating both spectral and spatial features improved the F1-score compared with using only spectral features. Additionally, we confirmed that the F1-score for late integration was slightly higher than that for middle integration.

Takao Kawamura

Yuma Kinoshita

Nobutaka Ono

Robin Scheibler

Acoustic scene classification using inter- and intra-subarray spatial features in distributed microphone array

基于分布式麦克风阵列的阵间与阵内空间特征声学场景分类

Abstract Mask-based multichannel speech enhancement methods based on artificial neural networks estimate a mask that is applied to the multichannel input signal or a reference channel to obtain the estimated desired signal. For the estimation, both spectral and spatial cues from the multichannel input can be used. However, the interplay of the two inside the neural network is typically unknown. In this contribution, we propose a framework to analyze neural spatiospectral filters (NSSFs) with respect to their capabilities to extract and represent spatial information. We explicitly take the characteristics of the training target signal into account and analyze its effect on the functionality of the NSSF. Using two conceptually different NSSFs as example, we show that not all NSSFs use spatial information under all circumstances and that the training target signal has a significant influence on the spatial filtering behavior of an NSSF. These insights help to assess the signal processing capabilities of neural networks and allow to make informed decisions when configuring, training, and deploying NSSFs.

Annika Briegleb

Walter Kellermann

Analysis of spatial filtering in neural spatiospectral filters and its dependence on training target characteristics

神经时空谱滤波器中空间滤波的分析及其对训练目标特征的依赖性

Abstract This manuscript deals with the task of real-time speaker diarization (SD) for stream-wise data processing. Therefore, in contrast to most of the existing papers, it considers not only the accuracy but also the computational demands of individual investigated methods. We first propose a new lightweight scheme allowing us to perform speaker diarization of streamed audio data. Our approach utilizes a modified residual network with squeeze-and-excitation blocks (SE-ResNet-34) to extract speaker embeddings in an optimized way using cached buffers. These embeddings are subsequently used for voice activity detection (VAD) and block-online k-means clustering with a look-ahead mechanism. The described scheme yields results similar to the reference offline system while operating solely on a CPU with a low real-time factor (RTF) below 0.1 and a constant latency of around 5.5 s. In the next part of the work, our research moves toward much more demanding and complex real-time processing of audio-visual data streams. For this purpose, we extend the above-mentioned scheme for audio data processing by adding an audio-video module. This module utilizes SyncNet combined with visual embeddings for identity tracking. Our resulting multi-modal SD framework then combines the outputs from audio and audio-video modules by using a new overlap-based fusion strategy. It yields diarization error rates that are competitive with the existing state-of-the-art offline audio-visual methods while allowing us to process various audio-video streams, e.g., from Internet or TV broadcasts, in real-time using GPU and with the same latency as for audio data processing.

Frantisek Kynych

Petr Cerva

Jindrich Zdansky

Torbjørn Svendsen

Giampiero Salvi

A lightweight approach to real-time speaker diarization: from audio toward audio-visual data streams

一种轻量级实时说话人日志方法：从音频到视听数据流

Abstract Speaker-conditioned target speaker extraction aims at estimating the target speaker from a mixture of speakers utilizing auxiliary information about the target speaker. In this paper, we consider a single-channel target speaker extraction system consisting of a speaker embedder network and a speaker separator network. Instead of using standard long short-term memory (LSTM) cells in the separator network, we propose two variants of LSTM cells that are customized for speaker-conditioned target speaker extraction. The first variant customizes both the forget gate and input gate of the LSTM cell, aiming at retaining only relevant features related to target speaker and disregarding the interfering speakers by simultaneously resetting and updating the cell state using the speaker embedding. For the second variant, we introduce a new gate within the LSTM cell, referred to as auxiliary-modulation gate. This gate modulates the information processing during cell state reset, aiming at learning the long-term and short-term discriminative features of the target speaker. Both in unidirectional and bidirectional mode, experimental results on 2-speaker mixtures, 3-speaker mixtures, and noisy mixtures (containing 1, 2, or 3 speakers) show that both proposed variants of LSTM cells outperform the standard LSTM cells for target speaker extraction, where the best performance is obtained using the auxiliary-gated LSTM cells.

Ragini Sinha

Christian Rollwage

Simon Doclo

Variants of LSTM cells for single-channel speaker-conditioned target speaker extraction

单通道说话人条件目标说话人提取的LSTM单元变体

Abbreviation	J AUDIO SPEECH MUSIC PROC.
Journal Impact	1.55
Quartiles(Global)	ENGINEERING, ELECTRICAL & ELECTRONIC(Q3)

ISSN	1687-4714, 1687-4722
h-index	34

Publication Information	Publisher: Springer Publishing Company，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2007，Proportion of original research papers: 87.10%，Self Citation Rate:11.80%， Gold OA Rate: 100.00%
Average review cycle	网友分享经验：>12周，或约稿
Average recruitment ratio	网友分享经验：容易

Eurasip Journal on Audio, Speech, and Music Processing

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Share Submission Experience

Improving speech recognition systems for the morphologically complex Malayalam language using subword tokens for language modeling

Domain-weighted transfer learning and discriminative embeddings for low-resource speaker verification

Acoustic scene classification using inter- and intra-subarray spatial features in distributed microphone array

Analysis of spatial filtering in neural spatiospectral filters and its dependence on training target characteristics

A lightweight approach to real-time speaker diarization: from audio toward audio-visual data streams

Acoustic scene classification using inter- and intra-subarray spatial features in distributed microphone array

Domain-weighted transfer learning and discriminative embeddings for low-resource speaker verification

Variants of LSTM cells for single-channel speaker-conditioned target speaker extraction

A lightweight approach to real-time speaker diarization: from audio toward audio-visual data streams

Analysis of spatial filtering in neural spatiospectral filters and its dependence on training target characteristics