Limited Domain Synthesis of Expressive Military Speech for Animated Characters

Johnson, W. L.; Narayanan, S.; Whitney, R.; Das, R.; Bulut, M.; LaBore, C.

doi:10.21236/ada459395

Cited by 33 publications

(11 citation statements)

References 8 publications

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“…Moreover, the development of limited domain synthesis specifically for the purposes of CALL is not feasible given the low levels of funding that CALL research and development receives. A notable exception is CALL for military training (Johnson et al, 2002).…”

Section: According To Van Bezooijen and Van Heuven This Definition Imentioning

confidence: 99%

Is text-to-speech synthesis ready for use in computer-assisted language learning?

Handley

2009

Speech Communication

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Text-to-Speech (TTS) synthesis, the generation of speech from text input, offers another means of providing spoken language input to learners in Computer-Assisted Language Learning (CALL) environments. Indeed, many potential benefits (ease of creation and editing of speech models, generation of speech models and feedback on demand, etc.) and uses (talking dictionaries, talking texts, dictation, pronunciation training, dialogue partner, etc.) of TTS synthesis in CALL have been put forward.Yet, the use of TTS synthesis in CALL is not widely accepted and only a few applications have found their way onto the market. One potential reason for this is that TTS synthesis has not been adequately evaluated for this purpose. Previous evaluations of TTS synthesis for use in CALL, have only addressed the comprehensibility of TTS synthesis. Yet, CALL places demands on the comprehensibility, naturalness, accuracy, register and expressiveness of the output of TTS synthesis. In this paper, the aforementioned aspects of the quality of the output of four state-of-the-art French TTS synthesis systems are evaluated with respect to their use in the three different roles that TTS synthesis systems may assume within CALL applications, namely: 1) reading machine, 2) pronunciation model and 3) conversational partner (Handley and Hamel, 2005). The results of this evaluation suggest that the best TTS synthesis systems are ready for use in applications in which they 'add value' to CALL, i.e. exploit the unique capacity of TTS synthesis to generate speech models on demand. An example of such an application is a dialogue 1

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Section: According To Van Bezooijen and Van Heuven This Definition Imentioning

confidence: 99%

Is text-to-speech synthesis ready for use in computer-assisted language learning?

Handley

2009

Speech Communication

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“…For applications like story speech synthesis, whether the prosody fits with the message is more important than subjects can classify the prosody without additional signs or cues [18]. Therefore, following [18] [19] proposed prosody rules are evaluated in a naturalistic context, using utterances which are clearly identifiable as fragments of children stories. As material, we have used 16 short utterances from four stories, taken from the stories used for analysis.…”

Section: Discussionmentioning

confidence: 99%

Telugu emotional story speech synthesis using SABLE markup language

Reddy

Harikrishna

Rao

et al. 2015

2015 International Conference on Signal Processing and Communication Engineering Systems

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In this paper, a framework for synthesizing Telugu emotional speech for story telling applications is presented. An XML based markup langauge, SABLE is used to synthesize the emotions from a given story text. SABLE markup defines a set of tags to improve the quality of the synthesized speech from the concatinative speech synthesizer. In this work, a subset of prosody tags are used to synthesize the emotional speech from a given story text. Modified Zero frequency filtered (ZFF) signal is used to derive the prosody correlates of pitch base, pitch range and intensity. The desired prosody modification factors for each emotion is derived at phrase level. The derived prosody modification parameters for each emotion are stored in the form of a template. During synthesis, hand annotated story text is replaced by prosody tags which are stored in templates. Prosody tagged story text at phrase level is automatically converted into SABLE markup format. The markup story text is used to synthesize emotional speech from the Telugu neutral Festival TTS system. The quality and naturalness of the synthesised emotional story speech is evaluated using subjective tests.

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“…The term "variety of styles" has been introduced as a domain-dependent point of study. For example, ESS can convey messages such as "good-bad news", "yes-no questions" [51], "storytelling" [52], and "military" [53].…”

Section: Expressive Speech Synthesismentioning

confidence: 99%

Prosodic mapping of text font based on the dimensional theory of emotions: a case study on style and size

Tsonos

2016

J AUDIO SPEECH MUSIC PROC.

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Current text-to-speech systems do not support the effective provision of the semantics and the cognitive aspects of the documents' typographic cues (e.g., font type, style, and size). A novel approach is introduced for the acoustic rendition of text font based on the emotional analogy between the visual (text font cues) and the acoustic (speech prosody) modalities. The methodology is based on: a) modeling reader's emotional state response ("Pleasure", "Arousal" and "Dominance") induced by the document's font cues and b) the acoustic mapping of the emotional state using expressive speech synthesis. A case study was conducted for the proposed methodology by calculating the prosodic values on specific font cues (several font styles and font sizes) and by examining listeners' preferences on the acoustic rendition of bold, italics, bold-italics, and various font sizes. The experimental results after the user evaluation indicate that the acoustic rendition of font size variations as well as bold and italics is recognized successfully, but bold-italics are confused with bold, due to the similarities of their prosodic variations.

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Limited Domain Synthesis of Expressive Military Speech for Animated Characters

Cited by 33 publications

References 8 publications

Is text-to-speech synthesis ready for use in computer-assisted language learning?

Is text-to-speech synthesis ready for use in computer-assisted language learning?

Telugu emotional story speech synthesis using SABLE markup language

Prosodic mapping of text font based on the dimensional theory of emotions: a case study on style and size

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