Maturation of Speech-in-Speech Recognition for Whispered and Voiced Speech

Buss, Emily; Miller, Margaret K.; Leibold, Lori J.

doi:10.1044/2022_jslhr-21-00620

Cited by 2 publications

(2 citation statements)

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“…Operating an application by voice is called "voice operation" [27]. "Speech recognition" may include the ability to identify the speaker.…”

Section: Target Example Of Pattern and Speech Recognitionmentioning

confidence: 99%

Development and Practical Applications of Computational Intelligence Technology

Matsuzaka,

Yashiro

2024

BioMedInformatics

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Computational intelligence (CI) uses applied computational methods for problem-solving inspired by the behavior of humans and animals. Biological systems are used to construct software to solve complex problems, and one type of such system is an artificial immune system (AIS), which imitates the immune system of a living body. AISs have been used to solve problems that require identification and learning, such as computer virus identification and removal, image identification, and function optimization problems. In the body’s immune system, a wide variety of cells work together to distinguish between the self and non-self and to eliminate the non-self. AISs enable learning and discrimination by imitating part or all of the mechanisms of a living body’s immune system. Certainly, some deep neural networks have exceptional performance that far surpasses that of humans in certain tasks, but to build such a network, a huge amount of data is first required. These networks are used in a wide range of applications, such as extracting knowledge from a large amount of data, learning from past actions, and creating the optimal solution (the optimization problem). A new technique for pre-training natural language processing (NLP) software ver.9.1by using transformers called Bidirectional Encoder Representations (BERT) builds on recent research in pre-training contextual representations, including Semi-Supervised Sequence Learning, Generative Pre-Training, ELMo (Embeddings from Language Models), which is a method for obtaining distributed representations that consider context, and ULMFit (Universal Language Model Fine-Tuning). BERT is a method that can address the issue of the need for large amounts of data, which is inherent in large-scale models, by using pre-learning with unlabeled data. An optimization problem involves “finding a solution that maximizes or minimizes an objective function under given constraints”. In recent years, machine learning approaches that consider pattern recognition as an optimization problem have become popular. This pattern recognition is an operation that associates patterns observed as spatial and temporal changes in signals with classes to which they belong. It involves identifying and retrieving predetermined features and rules from data; however, the features and rules here are not logical information, but are found in images, sounds, etc. Therefore, pattern recognition is generally conducted by supervised learning. Based on a new theory that deals with the process by which the immune system learns from past infection experiences, the clonal selection of immune cells can be viewed as a learning rule of reinforcement learning.

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“…Operating an application by voice is called "voice operation" [27]. "Speech recognition" may include the ability to identify the speaker.…”

Section: Target Example Of Pattern and Speech Recognitionmentioning

confidence: 99%

Development and Practical Applications of Computational Intelligence Technology

Matsuzaka,

Yashiro

2024

BioMedInformatics

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“…Speech perception is a difficult and complex task in naturalistic settings. Variables related to the speaker (e.g., accent), the listener (e.g., familiarity with the topic), and the environment (e.g., background noise) all influence the degree of difficulty that a listener may experience in perceiving and interpreting what they hear ( Freyman et al, 2004 ; Kidd et al, 2005 ; Desjardins and Doherty, 2013 ; Buss et al, 2022 ). As a result, speech perception draws on perceptual, linguistic, and cognitive processes to support recognition and interpretation of the speech stream.…”

Section: Introductionmentioning

confidence: 99%

Effects of linguistic context and noise type on speech comprehension

Fitzgerald,

DeDe,

Shen

2024

Front. Psychol.

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IntroductionUnderstanding speech in background noise is an effortful endeavor. When acoustic challenges arise, linguistic context may help us fill in perceptual gaps. However, more knowledge is needed regarding how different types of background noise affect our ability to construct meaning from perceptually complex speech input. Additionally, there is limited evidence regarding whether perceptual complexity (e.g., informational masking) and linguistic complexity (e.g., occurrence of contextually incongruous words) interact during processing of speech material that is longer and more complex than a single sentence. Our first research objective was to determine whether comprehension of spoken sentence pairs is impacted by the informational masking from a speech masker. Our second objective was to identify whether there is an interaction between perceptual and linguistic complexity during speech processing.MethodsWe used multiple measures including comprehension accuracy, reaction time, and processing effort (as indicated by task-evoked pupil response), making comparisons across three different levels of linguistic complexity in two different noise conditions. Context conditions varied by final word, with each sentence pair ending with an expected exemplar (EE), within-category violation (WV), or between-category violation (BV). Forty young adults with typical hearing performed a speech comprehension in noise task over three visits. Each participant heard sentence pairs presented in either multi-talker babble or spectrally shaped steady-state noise (SSN), with the same noise condition across all three visits.ResultsWe observed an effect of context but not noise on accuracy. Further, we observed an interaction of noise and context in peak pupil dilation data. Specifically, the context effect was modulated by noise type: context facilitated processing only in the more perceptually complex babble noise condition.DiscussionThese findings suggest that when perceptual complexity arises, listeners make use of the linguistic context to facilitate comprehension of speech obscured by background noise. Our results extend existing accounts of speech processing in noise by demonstrating how perceptual and linguistic complexity affect our ability to engage in higher-level processes, such as construction of meaning from speech segments that are longer than a single sentence.

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Maturation of Speech-in-Speech Recognition for Whispered and Voiced Speech

Cited by 2 publications

References 46 publications

Development and Practical Applications of Computational Intelligence Technology

Development and Practical Applications of Computational Intelligence Technology

Effects of linguistic context and noise type on speech comprehension

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