Simplified Audio Production in Asynchronous Voice-Based Discussions

Sivaraman, Venkatesh; Yoon, Dongwook; Mitros, Piotr

doi:10.1145/2858036.2858416

Cited by 13 publications

(18 citation statements)

References 16 publications

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“…For this reason, the transcripts only have to be sufficiently accurate to use for editing. Both Whittaker and Amento (2004) and Sivaraman et al (2016) found that the errors in the transcripts did not prevent users from being able to edit using them. However, both also found that users wanted to be able to fix incorrect words in the transcript.…”

Section: Transcriptsmentioning

confidence: 99%

“…Previous semantic speech editing system have used manual transcription ( Berthouzoz et al, 2012;Rubin et al, 2013 ), ASR ( Sivaraman et al, 2016;Whittaker and Amento, 2004 ) and alignment of subtitles ( Casares et al, 2002;Hyperaudio Inc., 2016 ). Manual transcriptions are nearly 100% accurate, however they are expensive (about $1 per minute) and slow (typically 24 hours).…”

Section: Automatic Speech Recognitionmentioning

confidence: 99%

“…No follow-up studies have been reported since. Sivaraman et al (2016) created a semantic editing system for asynchronous voice-based discussions, where users could quickly edit their speech recording before sending it to the recipient. Their system used near-live ASR and detected pauses in the speech.…”

Section: Introductionmentioning

confidence: 99%

“…This study demonstrates that for workflows that involve prewritten scripts, there is potential to improve the audio editing by using an integrated writing and editing system. ASR transcripts were used by Whittaker and Amento (2004) , Sivaraman et al (2016) , Shin et al (2016) and Yoon et al (2014) , but Berthouzoz et al (2012) and Rubin et al (2013) chose to use perfect transcripts from a crowd-sourcing service. Hyperaudio Inc. (2016) used aligned subtitles and Casares et al (2002) used a combination of subtitles and ASR.…”

Section: Introductionmentioning

confidence: 99%

“…Sivaraman et al (2016) found that for editing discussions, semantic editing is a more accessible alternative to waveform editing. Systems have been developed for audio and video production ( Berthouzoz et al, 2012;Casares et al, 2002;Rubin et al, 2013;Shin et al, 2016 ), but these were mostly designed without prior user requirements, and the studies were informal and used amateur participants.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Contextual Study of Semantic Speech Editing in Radio Production

Baume

Plumbley

Calic³

et al. 2018

International Journal of Human-Computer Studies

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a b s t r a c t Radio production involves editing speech-based audio using tools that represent sound using simple waveforms. Semantic speech editing systems allow users to edit audio using an automatically generated transcript, which has the potential to improve the production workflow. To investigate this, we developed a semantic audio editor based on a pilot study. Through a contextual qualitative study of five professional radio producers at the BBC, we examined the existing radio production process and evaluated our semantic editor by using it to create programmes that were later broadcast.We observed that the participants in our study wrote detailed notes about their recordings and used annotation to mark which parts they wanted to use. They collaborated closely with the presenter of their programme to structure the contents and write narrative elements. Participants reported that they often work away from the office to avoid distractions, and print transcripts so they can work away from screens. They also emphasised that listening is an important part of production, to ensure high sound quality. We found that semantic speech editing with automated speech recognition can be used to improve the radio production workflow, but that annotation, collaboration, portability and listening were not well supported by current semantic speech editing systems. In this paper, we make recommendations on how future semantic speech editing systems can better support the requirements of radio production.

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Section: Transcriptsmentioning

confidence: 99%

Section: Automatic Speech Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Contextual Study of Semantic Speech Editing in Radio Production

Baume

Plumbley

Calic³

et al. 2018

International Journal of Human-Computer Studies

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Digital ethnography of home use of digital personal assistants

Paay

Kjeldskov

Hansen

et al. 2020

Behaviour & Information Technology

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Commercialised voice user interface devices for the home, like Amazon Echo, Google Home, and Apple HomePod, with integrated digital personal assistants have rapidly grown in popularity. These devices embody intelligent software agents that support users in their everyday life through easy and intuitive conversational interactions. While their use in everyday activities is largely unexplored, the proliferation in home use presents a valuable opportunity to add to understanding around the use of in-home digital personal assistants. In this paper, we investigate their home use in a broad context to learn more about people's experiences, attitudes, interactions and expectations with these devices contributing new insights to current knowledge around this use. Applying the digital ethnography method, we collected 3,542 reviews and comments about Amazon Echo, Google Home, and Apple HomePod on Amazon, eBay, and Reddit. Six main themes and 29 categories were derived through filtering, thematic analysis and affinity diagramming. These findings constitute a conceptual framework characterising the current landscape of home use of digital personal assistants. Additionally, we identify and discuss unique issues discovered around the invisible interface, interactive freedom, and creative appropriation. We use our findings to propose implications for interaction design of DPAs for home use.

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The State of Speech in HCI: Trends, Themes and Challenges

Clark

Doyle

Garaialde

et al. 2019

Interacting With Computers

145

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Speech interfaces are growing in popularity. Through a review of 68 research papers this work maps the trends, themes, findings and methods of empirical research on speech interfaces in HCI. We find that most studies are usability/theory-focused or explore wider system experiences, evaluating Wizard of Oz, prototypes, or developed systems by using self-report questionnaires to measure concepts like usability and user attitudes. A thematic analysis of the research found that speech HCI work focuses on nine key topics: system speech production, modality comparison, user speech production, assistive technology & accessibility, design insight, experiences with interactive voice response (IVR) systems, using speech technology for development, people's experiences with intelligent personal assistants (IPAs) and how user memory affects speech interface interaction. From these insights we identify gaps and challenges in speech research, notably the need to develop theories of speech interface interaction, grow critical mass in this domain, increase design work, and expand research from single to multiple user interaction contexts so as to reflect current use contexts. We also highlight the need to improve measure reliability, validity and consistency, in the wild deployment and reduce barriers to building fully functional speech interfaces for research. Author Keywords Speech interfaces; speech HCI; review; speech technology; voice user interfaces Research Highlights• Most papers focused on usability/theory-based or wider system experience research with a focus on Wizard of Oz and developed systems, though a lack of design work • Questionnaires on usability and user attitudes often used but few were reliable or validated • Thematic analysis showed nine primary research topics • Gaps in research critical mass, speech HCI theories, and multiple user contexts

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Simplified Audio Production in Asynchronous Voice-Based Discussions

Cited by 13 publications

References 16 publications

A Contextual Study of Semantic Speech Editing in Radio Production

A Contextual Study of Semantic Speech Editing in Radio Production

Digital ethnography of home use of digital personal assistants

The State of Speech in HCI: Trends, Themes and Challenges

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