Paragraph Prosodic Patterns to Enhance Text-to-Speech Naturalness

2020 ◽

Vol 63 (7) ◽

pp. 2054-2069

Author(s):

Brandon Merritt ◽

Tessa Bent

Keyword(s):

Spontaneous Speech ◽

Identification Accuracy ◽

Rating Task ◽

Gender Identification ◽

Identification Task ◽

Male And Female ◽

Perceptual Evaluation ◽

Speech Training ◽

And Gender ◽

Speech Naturalness

Purpose The purpose of this study was to investigate how speech naturalness relates to masculinity–femininity and gender identification (accuracy and reaction time) for cisgender male and female speakers as well as transmasculine and transfeminine speakers. Method Stimuli included spontaneous speech samples from 20 speakers who are transgender (10 transmasculine and 10 transfeminine) and 20 speakers who are cisgender (10 male and 10 female). Fifty-two listeners completed three tasks: a two-alternative forced-choice gender identification task, a speech naturalness rating task, and a masculinity/femininity rating task. Results Transfeminine and transmasculine speakers were rated as significantly less natural sounding than cisgender speakers. Speakers rated as less natural took longer to identify and were identified less accurately in the gender identification task; furthermore, they were rated as less prototypically masculine/feminine. Conclusions Perceptual speech naturalness for both transfeminine and transmasculine speakers is strongly associated with gender cues in spontaneous speech. Training to align a speaker's voice with their gender identity may concurrently improve perceptual speech naturalness. Supplemental Material https://doi.org/10.23641/asha.12543158

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The Influence of Speaker and Listener Gender on Speech Naturalness Ratings: A Methodological Control Study

Contemporary Issues in Communication Science and Disorders ◽

10.1044/cicsd_26_f_106 ◽

1999 ◽

Vol 26 (Fall) ◽

pp. 106-110 ◽

Cited By ~ 3

Author(s):

Nicole L. Ermlich ◽

Dale Evan Metz ◽

Nicholas Schiavetti ◽

Robert L. Whitehead

Keyword(s):

Control Study ◽

Speech Naturalness

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A Review of 21 iPad Applications for Augmentative and Alternative Communication Purposes

Perspectives on Augmentative and Alternative Communication ◽

10.1044/aac21.2.60 ◽

2012 ◽

Vol 21 (2) ◽

pp. 60-71 ◽

Cited By ~ 24

Author(s):

Ashley Alliano ◽

Kimberly Herriger ◽

Anthony D. Koutsoftas ◽

Theresa E. Bartolotta

Keyword(s):

Augmentative And Alternative Communication ◽

Cost Effective ◽

Alternative Form ◽

Alternative Communication ◽

Text To Speech ◽

Reference Guide ◽

Expressive Communication ◽

Communication Needs ◽

User Friendly ◽

Ipad Applications

Abstract Using the iPad tablet for Augmentative and Alternative Communication (AAC) purposes can facilitate many communicative needs, is cost-effective, and is socially acceptable. Many individuals with communication difficulties can use iPad applications (apps) to augment communication, provide an alternative form of communication, or target receptive and expressive language goals. In this paper, we will review a collection of iPad apps that can be used to address a variety of receptive and expressive communication needs. Based on recommendations from Gosnell, Costello, and Shane (2011), we describe the features of 21 apps that can serve as a reference guide for speech-language pathologists. We systematically identified 21 apps that use symbols only, symbols and text-to-speech, and text-to-speech only. We provide descriptions of the purpose of each app, along with the following feature descriptions: speech settings, representation, display, feedback features, rate enhancement, access, motor competencies, and cost. In this review, we describe these apps and how individuals with complex communication needs can use them for a variety of communication purposes and to target a variety of treatment goals. We present information in a user-friendly table format that clinicians can use as a reference guide.

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Influence of Analog Versus Digital Recordings on Speech Naturalness Judgments

Contemporary Issues in Communication Science and Disorders ◽

10.1044/cicsd_25_s_45 ◽

1998 ◽

Vol 25 (Spring) ◽

pp. 45-49

Author(s):

Kimberly Farinella

Keyword(s):

Speech Naturalness ◽

Digital Recordings

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Design of English text-to-speech conversion algorithm based on machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189238 ◽

2020 ◽

pp. 1-12

Author(s):

Li Dongmei

Keyword(s):

Machine Learning ◽

Speech Synthesis ◽

Feature Recognition ◽

Learning Algorithm ◽

Morphological Structure ◽

English Text ◽

Text To Speech ◽

Part Of Speech ◽

Modern Computer ◽

Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

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