Automatic speech recognition for generalised time based media retrieval and indexing

Robertson, J. Ian S.; Wong, Wai Yat; Chung, Charles Y. C.; Kim, Yon Su

doi:10.1145/290747.290777

Cited by 5 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The list of applications of automatic speech recognition is so long and is growing; some of known applications include virtual reality, Multimedia searches, auto-attendants, travel information and reservation, translators, natural language understanding and many more applications [5,6].…”

Section: Automatic Speech Recognitionmentioning

confidence: 99%

A Review of Challenges in Automatic Speech Recognition

Petkar¹

2016

IJCA

View full text Add to dashboard Cite

Speech is the nature's gift to the human being which contributes towards the intelligence and discrimination from rest of the animal kingdom. Taking into consideration technological aspects, speech recognition is the buzzword today, as communication and hands free computing evolving day by day. Speech is a very important mode of the communication and interaction with the digital computer. Speech recognition along with the wide range of applicability in domain of computer science, medical science, psychology, sports, neurology has many challenges while developing. Developing real time speech recognizer may hurdle from adverse environment to anatomy of the human body. It also involves linguistic aspects too. This paper explores various challenges in developing a robust ASR system.

show abstract

Section: Automatic Speech Recognitionmentioning

confidence: 99%

A Review of Challenges in Automatic Speech Recognition

Petkar¹

2016

IJCA

View full text Add to dashboard Cite

show abstract

“…It can serve as a tool to support content-based retrieval of audio and audio/visual data [35] and information extraction [13]. Business benefits derived from speech recognition include significantly lowered operational costs; decreased "dropped" calls; improved business agility, customer satisfaction, and loyalty; and increased productivity [8,19,30].…”

Section: Benefits and Challenges Of The Speech Recognition Technologymentioning

confidence: 99%

Discovering Cues to Error Detection in Speech Recognition Output: A User-Centered Approach

Zhou¹,

Shi²,

Zhang³

et al. 2006

Journal of Management Information Systems

View full text Add to dashboard Cite

The great potential of speech recognition systems in freeing users' hands while interacting with computers has inspired a variety of promising applications. However, given the performance of the state-of-the-art speech recognition technology today, widespread acceptance of speech recognition technology would not be realistic without designing and developing new approaches to detecting and correcting recognition errors effectively. In seeking solutions to the above problem, identifying cues to error detection (CERD) is central. Our survey of the extant literature on the detection and correction of speech recognition errors reveals that the systeminitiated, data-driven approach is dominant, but that heuristics from human users have been largely overlooked. This may have hindered the advance of speech technology. In this research, we propose a user-centered approach to discovering CERD. User studies are carried out to implement the approach. Content analysis of the collected verbal protocols lends itself to a taxonomy of CERD. The CERD discovered in this study can improve our knowledge on CERD by not only validating CERD from a user's perspective but also suggesting promising new CERD for detecting speech recognition errors. Moreover, the analysis of CERD in relation to error types and other CERD provides new insights into the context where specific CERD are effective. The findings of this study can be used to not only improve speech recognition output but also to provide context-aware support for error detection. This will help break the barrier for mainstream adoption of speech technology in a variety of information systems and applications.KEY WORDS AND PHRASES: cues to error detection, speech recognition, taxonomy, verbal protocol analysis.SPEECH RECOGNITION IS ONE OF THE MAIN information technologies that provide users with hands-free interaction with computers. The power of speech control promises to help many individuals who might otherwise not be able to interact with a computer through the conventional mouse or keyboard interface. Particularly, users who are physically challenged, visually impaired, or mobility impaired are offered new opportunities by speech recognition-enabled applications.Although many interesting applications have emerged or enhanced with the advance of speech technology, the current use of such a technology reflects "only a tip of the iceberg of the full power that the technology could potentially offer" (cf. [8, p. 69]). However, some fundamental and practical limitations of the technology result in unsatisfactory performance of speech recognition systems. The convenience and efficiency promised by speech technology in interacting with computers is seriously compromised by the laborious efforts and frustration experienced in detecting and correcting recognition errors [40]. Widespread acceptance of speech as a primary input modality for computers will not be possible unless the underlying recognition technology can produce sufficiently robust and low-error output [8].To br...

show abstract

“…Transcription systems are wide and varied in their uses, which include dictation [23] as well as audio [8,7], multimedia [6,25,18] and meeting indexing applications [24,22]. However, since even the most sophisticated automatic speech recognition systems are far from perfect, transcriptions produced by their application often contain errors.…”

Section: Introductionmentioning

confidence: 99%

Interactive visualisation techniques for dynamic speech transcription, correction and training

Luz

Masoodian

Rogers

2008

Proceedings of the 9th ACM SIGCHI New Zealand Chapter's International Conference on Human-Computer Interaction: Design Centered

View full text Add to dashboard Cite

As performance gains in automatic speech recognition systems plateau, improvements to existing applications of speech recognition technology seem more likely to come from better user interface design than from further progress in core recognition components. Among all applications of speech recognition, the usability of systems for transcription of spontaneous speech is particularly sensitive to high word error rates. This paper presents a series of approaches to improving the usability of such applications. We propose new mechanisms for error correction, use of contextual information, and use of 3D visualisation techniques to improve user interaction with a recogniser and maximise the impact of user feedback. These proposals are illustrated through several prototypes which target tasks such as: off-line transcript editing, dynamic transcript editing, and real-time visualisation of recognition paths. An evaluation of our dynamic transcript editing system demonstrates the gains that can be made by adding the corrected words to the recogniser's dictionary and then propagating the user's corrections.

show abstract

Automatic speech recognition for generalised time based media retrieval and indexing

Cited by 5 publications

References 14 publications

A Review of Challenges in Automatic Speech Recognition

A Review of Challenges in Automatic Speech Recognition

Discovering Cues to Error Detection in Speech Recognition Output: A User-Centered Approach

Interactive visualisation techniques for dynamic speech transcription, correction and training

Contact Info

Product

Resources

About