A Japanese spontaneous speech corpus collected using automatically inferencing Wizard of OZ system.

Itou, Katunobu; Akiba, Tomoyosi; Hasegawa, Osamu; Hayamizu, Satoru; Tanaka, Kensuke

doi:10.1250/ast.20.207

Cited by 5 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each utterance contained at least one filled pause. Those utterances were excerpted from a spontaneous speech dialogue corpus [12] collected using a Wizard of OZ system and were automatically segmented by detecting each silence interval longer than 300 ms.…”

Section: Resultsmentioning

confidence: 99%

A real-time filled pause detection system for spontaneous speech recognition

Goto¹,

Itou²,

Hayamizu³

1999

6th European Conference on Speech Communication and Technology

Self Cite

View full text Add to dashboard Cite

This paper describes a method for automatically detecting filled (vocalized) pauses, which are one of the hesitation phenomena that current speech recognizers typically cannot handle. The detection of these pauses is important in spontaneous speech dialogue systems because they play valuable roles, such as helping a speaker keep a conversational turn, in oral communication. Although a few speech recognition systems have processed filled pauses within subword-based connected word recognition or word-spotting frameworks, they did not detect the pauses individually and consequently could not consider their roles. In this paper we propose a method that detects filled pauses and word lengthening on the basis of small fundamental frequency transition and small spectral envelope deformation under the assumption that speakers do not change articulator parameters during filled pauses. Experimental results for a Japanese spoken dialogue corpus show that our real-time filled-pause-detection system yielded a recall rate of 84.9% and a precision rate of 91.5%.

show abstract

Section: Resultsmentioning

confidence: 99%

A real-time filled pause detection system for spontaneous speech recognition

Goto¹,

Itou²,

Hayamizu³

1999

6th European Conference on Speech Communication and Technology

Self Cite

View full text Add to dashboard Cite

show abstract

“…The results were attributed to the current conservative setting, which is used to avoid recognition errors that might occur when a presenter utters long vowels, such as "volunteer". Figure 9 is supplemental data showing our baseline results for the precision and recall rates obtained by applying our algorithm to a Japanese spontaneous speech corpus [13] while changing the parameter Th fp . Although a simple comparison between the results in Figure 9 and Table 1 is difficult because the recording environments differed, the results do indicate that precision and recall can be adjusted to some extent by changing the parameter Th fp to meet users' requests to be conservative or aggressive.…”

Section: Effectiveness and Suggestions For Improvementsmentioning

confidence: 99%

Presentation sensei

Kurihara

Goto

Ogata

et al. 2007

Proceedings of the 9th International Conference on Multimodal Interfaces

View full text Add to dashboard Cite

In this paper we present a presentation training system that observes a presentation rehearsal and provides the speaker with recommendations for improving the delivery of the presentation, such as to speak more slowly and to look at the audience. Our system "Presentation Sensei" is equipped with a microphone and camera to analyze a presentation by combining speech and image processing techniques. Based on the results of the analysis, the system gives the speaker instant feedback with respect to the speaking rate, eye contact with the audience, and timing. It also alerts the speaker when some of these indices exceed predefined warning thresholds. After the presentation, the system generates visual summaries of the analysis results for the speaker's selfexaminations. Our goal is not to improve the content on a semantic level, but to improve the delivery of it by reducing inappropriate basic behavior patterns. We asked a few test users to try the system and they found it very useful for improving their presentations. We also compared the system's output with the observations of a human evaluator. The result shows that the system successfully detected some inappropriate behavior. The contribution of this work is to introduce a practical recognitionbased human training system and to show its feasibility despite the limitations of state-of-the-art speech and video recognition technologies.

show abstract

Wizard of Oz Method for Constructing Conversational Web Agents

昌之

信敏

2002

Transactions of the Japanese Society for Artificial Intelligenc

View full text Add to dashboard Cite

We propose a framework to construct conversational Web agents, which guide the visitors of Web sites, from actual dialogues incrementally. We apply the Wizard of Oz method enhanced by adding the functionality of learning dialogue models to the process. In this method, a developer collects annotated dialogues by chatting with users. At first, the developer has to input almost all replies. As the learning proceeds, the system infers proper utterances and the load of developer is reduced. Finally, a conversational agent is constructed. We developed a system to construct such kind of agents in the Web environment. The features of this system are: (a) FSM-based dialogue models and incremental algorithms of learning probabilistic DFAs; (b) using annotations for the meaning of each utterance and for contents associated with Web pages; and (c) a character interface with speech functionality. We also examined how the developer's cost is reduced as the growth of the dialogue models, applying it to Kyoto tour guide task.

show abstract

A Japanese spontaneous speech corpus collected using automatically inferencing Wizard of OZ system.

Cited by 5 publications

References 4 publications

A real-time filled pause detection system for spontaneous speech recognition

A real-time filled pause detection system for spontaneous speech recognition

Presentation sensei

Wizard of Oz Method for Constructing Conversational Web Agents

Contact Info

Product

Resources

About