1,189 research outputs found
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks
About Voice: A Longitudinal Study of Speaker Recognition Dataset Dynamics
Like face recognition, speaker recognition is widely used for voice-based
biometric identification in a broad range of industries, including banking,
education, recruitment, immigration, law enforcement, healthcare, and
well-being. However, while dataset evaluations and audits have improved data
practices in computer vision and face recognition, the data practices in
speaker recognition have gone largely unquestioned. Our research aims to
address this gap by exploring how dataset usage has evolved over time and what
implications this has on bias and fairness in speaker recognition systems.
Previous studies have demonstrated the presence of historical, representation,
and measurement biases in popular speaker recognition benchmarks. In this
paper, we present a longitudinal study of speaker recognition datasets used for
training and evaluation from 2012 to 2021. We survey close to 700 papers to
investigate community adoption of datasets and changes in usage over a crucial
time period where speaker recognition approaches transitioned to the widespread
adoption of deep neural networks. Our study identifies the most commonly used
datasets in the field, examines their usage patterns, and assesses their
attributes that affect bias, fairness, and other ethical concerns. Our findings
suggest areas for further research on the ethics and fairness of speaker
recognition technology.Comment: 14 pages (23 with References and Appendix
Towards Single-Channel Speech Separation in Noise and Reverberation
Many speech technologies, such as automatic speech recognition and speaker identification, are conventionally designed to only work on single speech streams. As a result, these systems can suffer severely degraded performance in cases of overlapping speech, i.e. when two or more people are speaking at the same time. Speech separation systems aim to address this problem by taking a recording of a speech mixture and outputting a single recording for each speaker in the mixture, where the interfering speech has been removed. The advancements in speech technology provided by deep neural networks have extended to speech separation, resulting in the first effectively functional single-channel speech separation systems. As performance of these systems has improved, there has been a desire to extend their capabilities beyond the clean studio recordings using close-talking microphones that the technology was initially developed on. In this dissertation, we focus on the extension of these technologies to the noisy and reverberant conditions more representative of real-world applications. Contributions of this dissertation include producing and releasing new data appropriate for training and evaluation of single-channel speech separation techniques, performing benchmark experiments to establish the degradation of conventional methods in more realistic settings, theoretical analysis of the impact, and development of new techniques targeted at improving system performance in these adverse conditions
Register Variation Remains Stable Across 60 Languages
This paper measures the stability of cross-linguistic register variation. A
register is a variety of a language that is associated with extra-linguistic
context. The relationship between a register and its context is functional: the
linguistic features that make up a register are motivated by the needs and
constraints of the communicative situation. This view hypothesizes that
register should be universal, so that we expect a stable relationship between
the extra-linguistic context that defines a register and the sets of linguistic
features which the register contains. In this paper, the universality and
robustness of register variation is tested by comparing variation within vs.
between register-specific corpora in 60 languages using corpora produced in
comparable communicative situations: tweets and Wikipedia articles. Our
findings confirm the prediction that register variation is, in fact, universal
Speech Enhancement for Automatic Analysis of Child-Centered Audio Recordings
Analysis of child-centred daylong naturalist audio recordings has become a de-facto research protocol in the scientific study of child language development. The researchers are increasingly using these recordings to understand linguistic environment a child encounters in her routine interactions with the world. These audio recordings are captured by a microphone that a child wears throughout a day. The audio recordings, being naturalistic, contain a lot of unwanted sounds from everyday life which degrades the performance of speech analysis tasks. The purpose of this thesis is to investigate the utility of speech enhancement (SE) algorithms in the automatic analysis of such recordings. To this effect, several classical signal processing and modern machine learning-based SE methods were employed 1) as a denoiser for speech corrupted with additive noise sampled from real-life child-centred daylong recordings and 2) as front-end for downstream speech processing tasks of addressee classification (infant vs. adult-directed speech) and automatic syllable count estimation from the speech. The downstream tasks were conducted on data derived from a set of geographically, culturally, and linguistically diverse child-centred daylong audio recordings. The performance of denoising was evaluated through objective quality metrics (spectral distortion and instrumental intelligibility) and through the downstream task performance. Finally, the objective evaluation results were compared with downstream task performance results to find whether objective metrics can be used as a reasonable proxy to select SE front-end for a downstream task. The results obtained show that a recently proposed Long Short-Term Memory (LSTM)-based progressive learning architecture provides maximum performance gains in the downstream tasks in comparison with the other SE methods and baseline results. Classical signal processing-based SE methods also lead to competitive performance. From the comparison of objective assessment and downstream task performance results, no predictive relationship between task-independent objective metrics and performance of downstream tasks was found
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