Improving Children Speech Recognition through Feature Learning from Raw Speech Signal

Dubagunta, S. Pavankumar; Kabil, Selen Hande; Magimai.-Doss, Mathew

doi:10.1109/icassp.2019.8682826

Cited by 14 publications

(4 citation statements)

References 10 publications

(13 reference statements)

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“…All of these features decrease the performance of common speech recognition systems, as shown in Kennedy et al (2017). While there are efforts to increase speech recognition performance to account for children's acoustic variability, such as Dubagunta et al (2019), Gale et al (2019), Wu et al (2019), or Shivakumar and Georgiou (2020), other above-mentioned inconsistencies can still prevent speech-based systems from understanding or fulfilling the communicative intent of a child in spite of the low word recognition error rate, which could be observed in the study by Lovato et al (2019). Despite all these issues, a lot of children have access to and engage with voice assistants in their everyday lives.…”

Section: Introductionmentioning

confidence: 99%

“Alexa, You're Really Stupid”: A Longitudinal Field Study on Communication Breakdowns Between Family Members and a Voice Assistant

Маврина

Szczuka

Strathmann

et al. 2022

Front. Comput. Sci.

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In this paper, we present the results of our long-term study on use of a voice assistant (Amazon Alexa via Amazon Echo Dot) in nine families with children and no previous experience with this technology. The study was conducted over the course of 5 weeks during which the families could interact with the device freely. Three house visits were made to collect empirical data from the adult participants in form of questionnaires. Additionally, conversational data from log files of the voice assistant were obtained. These data were annotated and analyzed with a focus on communication breakdowns during human-assistant interaction. We investigate user behavior for both adults and children in such situations, its reasons and consequences for user satisfaction. This article provides qualitative analysis of three particularly interesting breakdown cases, as well as statistical analysis along several hypotheses and research questions combining empirical and conversational data. Described cases of communication breakdown illustrate findings from existing literature on the topic. The statistical analysis paints a mixed picture, however, it helped us identify further avenues for research, some of which can be explored with our data set in the future. We found a significant negative effect of the number of abandoned failed requests on user satisfaction, contrary to the number of successfully repaired requests that had no influence on user satisfaction. We discovered that users are more inclined to use reformulation as repair strategy when they do not perceive the emergence of miscommunication as their fault. We could not identify a significant effect of internal reasons for the choice of other strategies, so we suggest that situational clues such as the immediate response of the voice assistant are more important for the choice of repair strategy. Our results also hint that users distinguish between repair strategies differently, as the self-perceived frequency of repetitions and abortions of requests were found to be positive predictors for the use of reformulation-based strategies. With regards to the long-term aspect of the study, use of repetition as a repair strategy by both children and adults significantly decreased with time, no other changes were found for other strategies. Additionally, no significant impact of age on the choice of repair strategy was found, as well as no interaction effect between age and time.

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Section: Introductionmentioning

confidence: 99%

“Alexa, You're Really Stupid”: A Longitudinal Field Study on Communication Breakdowns Between Family Members and a Voice Assistant

Маврина

Szczuka

Strathmann

et al. 2022

Front. Comput. Sci.

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“…ASR systems like Alexa, Siri, and Google should have been an asset to such growth, providing a more frictionless interaction with technology. However, children's speech recognition seems to be a tough nut to crack due to the acoustic and linguistic variability that a child's speech often brings to the table [130]. ASR for child speech is proven more challenging than that for adult speech, due to children's shorter vocal tracts, slower and more variable speaking rate and inaccurate articulation [131].…”

Section: Children-centric Focusmentioning

confidence: 99%

Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems

Basak¹,

Agrawal²,

Jena³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Speech recognition systems have become a unique human-computer interaction (HCI) family. Speech is one of the most naturally developed human abilities; speech signal processing opens up a transparent and hand-free computation experience. This paper aims to present a retrospective yet modern approach to the world of speech recognition systems. The development journey of ASR (Automatic Speech Recognition) has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper. A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented, along with a brief discussion of various modern-day developments and applications in this domain. This review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal processing. Since speech recognition has a vast potential in various industries like telecommunication, emotion recognition, healthcare, etc., this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution. KEYWORDSSpeech recognition; automatic speech recognition (ASR); mel-frequency cepstral coefficients (MFCC); hidden Markov model (HMM); artificial neural network (ANN)

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“…This is improvised using CNN-based end-to-end acoustic modelling methods. For feature extraction, MFCC is used in this system [22].…”

Section: Review Of Literaturementioning

confidence: 99%

Speech Recognition Implementation

2019

IJITEE

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This paper presents a brief review on Automatic Speech Recognition and provide a technical understanding of ASR system. The objective of this review paper is to elaborate one of the best techniques in the field of speech recognition that is hidden Markov model. Hidden Markov model is very popular technique for speech recognition because speech signal is more like piecewise stationary or short time stationary signal and these models can be trained easily and they are computationally feasible. So, this paper gives a proper implementation of hidden Markov model. After so many years of research, the main challenge in speech recognition field is accuracy. The speech recognition system includes feature extraction, building word template, comparing word and selecting the best with maximum likelihood. Hence, this paper will give a great contribution for understanding the concepts of Automatic Speech Recognition system and hidden Markov model.

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Improving Children Speech Recognition through Feature Learning from Raw Speech Signal

Cited by 14 publications

References 10 publications

“Alexa, You're Really Stupid”: A Longitudinal Field Study on Communication Breakdowns Between Family Members and a Voice Assistant

“Alexa, You're Really Stupid”: A Longitudinal Field Study on Communication Breakdowns Between Family Members and a Voice Assistant

Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems

Speech Recognition Implementation

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