Using confidence scores to improve hands-free speech based navigation in continuous dictation systems

Feng, Jinjuan; Sears, Andrew

doi:10.1145/1035575.1035576

Cited by 41 publications

(30 citation statements)

References 18 publications

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“…Our earlier studies, using the same recognition engine with various tasks, resulted in RERs ranging from 10 to 19% [e.g., 15,14,35]. We suggest that the substantially higher RERs observed in the current study (e.g., 33-44%) are due, at least in part, to the addition of background noise.…”

Section: Discussionsupporting

confidence: 51%

Motion does matter: an examination of speech-based text entry on the move

Price¹,

Lin²,

Feng³

et al. 2005

Univ Access Inf Soc

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Desktop interaction solutions are often inappropriate for mobile devices due to small screen size and portability needs. Speech recognition can improve interactions by providing a relatively hands-free solution that can be used in various situations. While mobile systems are designed to be transportable, few have examined the effects of motion on mobile interactions. This paper investigates the effect of motion on automatic speech recognition (ASR) input for mobile devices. Speech recognition error rates (RER) have been examined with subjects walking or seated, while performing text input tasks and the effect of ASR enrollment conditions on RER. The obtained results suggest changes in user training of ASR systems for mobile and seated usage.

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Section: Discussionsupporting

confidence: 51%

Motion does matter: an examination of speech-based text entry on the move

Price¹,

Lin²,

Feng³

et al. 2005

Univ Access Inf Soc

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“…In addition, the second-best hypothesis was used by some systems to replace the best hypothesis that was possibly wrong in error correction [27]. Preceding error echoes the observations of other studies [7,12,17].…”

Section: Summary Of Cerd and Discussionmentioning

confidence: 67%

“…The transcripts were randomly extracted from a dictation corpus that was generated by a commercial speech recognition system under high-quality conditions from the spontaneous speech of 27 speakers [12,39]. All of the speakers were native, but not professional, English speakers.…”

Section: Transcript Datamentioning

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

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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...

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“…Recognition errors may appear in isolation or in sequence with results of one study confirming that over 70% of errors appeared immediately adjacent to at least one other recognition error (Feng and Sears, 2004). Consecutive errors could be caused by incorrect segmentation of acoustic signals or the language models widely adopted in speech recognition systems.…”

Section: Consecutive Error Ratementioning

confidence: 92%

“…How individuals perceive the process and outcomes they produce can be important indicators of the success of a decision support technique (Schweiger et al, 1989). User perceptions have already been used to evaluate a variety of speechbased interaction solutions (Feng and Sears, 2004), and we expect to improve the perceived usefulness by providing support mechanisms.…”

Section: Error Detection Supportmentioning

confidence: 99%

Third-party error detection support mechanisms for dictation speech recognition

Zhou

Shi

Sears

2010

Interacting with Computers

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a b s t r a c tAlthough speech recognition has improved significantly in recent years, its adoption continues to be limited, in part, by the effort and frustration associated with correcting speech recognition errors. Error detection is a particularly challenging issue in third-party error correction where different individuals are responsible for the original dictation and correcting the resulting text. This research aims to address the difficulty experienced in third-party error detection by developing and evaluating a variety of support mechanisms. Drawing on a growing body of literature on human computer interaction and speech recognition, four support mechanisms were designed and evaluated, namely indexed audio, speech summarization, error prediction, and the presentation of alternative hypotheses. A user study assessed the impact of these support mechanisms on both performance and perceptions during error detection tasks. Performance measures included effectiveness and efficiency, and perception measures included confidence, perceived usefulness, and cognitive workload. The results provide strong support for the use of indexed audio in the context of third-party error detection. The results also confirm that consecutive error rate, or the percentage of recognition errors immediately adjacent to another error, has a negative impact on the effectiveness of third-party error detection. Other support mechanisms failed to improve either effectiveness or perceptions, but they did negate the negative impact as consecutive error rate increased. These findings have significant implications for speech recognition error detection research and the design of error detection support solutions.

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Using confidence scores to improve hands-free speech based navigation in continuous dictation systems

Cited by 41 publications

References 18 publications

Motion does matter: an examination of speech-based text entry on the move

Motion does matter: an examination of speech-based text entry on the move

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

Third-party error detection support mechanisms for dictation speech recognition

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