The IEEE Transactions on Audio, Speech and Language Processing covers the sciences, technologies and applications relating to the analysis, coding, enhancement, recognition and synthesis of audio, music, speech and language. In particular, audio processing also covers auditory modeling, acoustic modeling and source separation. Speech processing also covers speech production and perception, adaptation, lexical modeling and speaker recognition. Language processing also covers spoken language understanding, translation, summarization, mining, general language modeling, as well as spoken dialog systems.

The IEEE Transactions on Audio, Speech and Language Processing covers the sciences, technologies, and applications related to the analysis, coding, enhancement, recognition, and synthesis of audio, music, speech, and language. Specifically, audio processing includes auditory modeling, acoustic modeling, and source separation. Speech processing encompasses speech production and perception, adaptation, lexical modeling, and speaker recognition. Language processing involves spoken language understanding, translation, summarization, mining, general language modeling, and spoken dialog systems.

《IEEE Transactions on Audio, Speech and Language Processing》涵盖了与音频、音乐、语音和语言的分析、编码、增强、识别和合成相关的科学、技术和应用。具体而言，音频处理还包括听觉建模、声学建模和源分离。语音处理涉及语音的产生和感知、适应、词汇建模和说话人识别。语言处理则包括口语理解、翻译、摘要、挖掘、通用语言建模以及口语对话系统。

IEEE Transactions on Audio, Speech and Language Processing

This paper investigates robust privacy-sensitive audio features for speaker diarization in multiparty conversations: i.e., a set of audio features having low linguistic information for speaker diarization in a single and multiple distant microphone scenarios. We systematically investigate Linear Prediction (LP) residual. Issues such as prediction order and choice of representation of LP residual are studied. Additionally, we explore the combination of LP residual with subband information from 2.5 kHz to 3.5 kHz and spectral slope. Next, we propose a supervised framework using deep neural architecture for deriving privacy-sensitive audio features. We benchmark these approaches against the traditional Mel Frequency Cepstral Coefficients (MFCC) features for speaker diarization in both the microphone scenarios. Experiments on the RT07 evaluation dataset show that the proposed approaches yield diarization performance close to the MFCC features on the single distant microphone dataset. To objectively evaluate the notion of privacy in terms of linguistic information, we perform human and automatic speech recognition tests, showing that the proposed approaches to privacy-sensitive audio features yield much lower recognition accuracies compared to MFCC features.

Sree Hari Krishnan Parthasarathi

H. Bourlard

D. Gatica-Perez

IEEE Transactions on Audio Speech and Language Processing

Wordless Sounds: Robust Speaker Diarization Using Privacy-Preserving Audio Representations

无词之声：利用隐私保护音频表征进行鲁棒的说话人日志分析

In this paper, we evaluate the performance of several objective measures in terms of predicting the quality of noisy speech enhanced by noise suppression algorithms. The objective measures considered a wide range of distortions introduced by four types of real-world noise at two signal-to-noise ratio levels by four classes of speech enhancement algorithms: spectral subtractive, subspace, statistical-model based, and Wiener algorithms. The subjective quality ratings were obtained using the ITU-T P.835 methodology designed to evaluate the quality of enhanced speech along three dimensions: signal distortion, noise distortion, and overall quality. This paper reports on the evaluation of correlations of several objective measures with these three subjective rating scales. Several new composite objective measures are also proposed by combining the individual objective measures using nonparametric and parametric regression analysis techniques.

Yi Hu

Philipos C. Loizou

IEEE Trans. Audio Speech Lang. Process.

Evaluation of Objective Quality Measures for Speech Enhancement

语音增强客观质量评估

In the past few years, objective quality assessment models have become increasingly used for assessing or monitoring speech and audio quality. By measuring perceived quality on an easily-understood subjective scale, such as listening quality (excellent, good, fair, poor, bad), these methods provide a quick and repeatable way to estimate customer experience. Typical applications include audio quality evaluation, selection of codecs or other equipment, and measuring the quality of telephone networks. To introduce this special issue, this paper provides an overview of the field, outlining the main approaches to intrusive, nonintrusive and parametric models and discussing some of their limitations and areas of future work

A.W. Rix

J.G. Beerends

D.-S. Kim

P. Kroon

O. Ghitza

Objective Assessment of Speech and Audio Quality—Technology and Applications

语音与音频质量的客观评估——技术与应用

Zhao Rui

Mao Kezhi

文章分類のための話題を意識した深い組成モデル【Powered by NICT】

用于文章分类的以话题为导向的深度组合模型【Powered by NICT】

Li Kun

Qian Xiaojun

Meng Helen

Multidistributionディープニューラルネットワークを用いたL2英語音声におけるMispronunciation検出と診断【Powered by NICT】

使用多分布深度神经网络的L2英语语音中的发音错误检测与诊断【由NICT提供支持】

Christian Fischer Pedersen

Paul Dalsgaard

Ove Andersen

On separability of the opening/closing phases of the LF glottal flow model by zeros of the z-transform

关于通过z变换零点分离LF声门流模型开闭相位的问题

关于LF声门流模型开闭相位可分离性的z变换零点分析

关于LF声门流模型开闭相位可分性的z变换零点分析

Publication Information	Publisher: Institute of Electrical and Electronics Engineers Inc.，Publishing cycle: Bimonthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2006，Proportion of original research papers: 0.00%，Self Citation Rate:N.A.， Gold OA Rate: 0.00%
Average review cycle	网友分享经验：平均24.0个月
Average recruitment ratio	网友分享经验：约25%

IEEE Transactions on Audio, Speech and Language Processing

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Wordless Sounds: Robust Speaker Diarization Using Privacy-Preserving Audio Representations

Evaluation of Objective Quality Measures for Speech Enhancement

Objective Assessment of Speech and Audio Quality—Technology and Applications

文章分類のための話題を意識した深い組成モデル【Powered by NICT】

Multidistributionディープニューラルネットワークを用いたL2英語音声におけるMispronunciation検出と診断【Powered by NICT】

On separability of the opening/closing phases of the LF glottal flow model by zeros of the z-transform

On separability of the opening/closing phases of the LF glottal flow model by zeros of the z-transform

On separability of the opening/closing phases of the LF glottal flow model by zeros of the z-transform

On separability of the opening/closing phases of the LF glottal flow model by zeros of the z-transform

On separability of the opening/closing phases of the LF glottal flow model by zeros of the z-transform