The Relative Frequency of Phonemes in General-American English

Hayden, Rebecca

doi:10.1080/00437956.1950.11659381

Cited by 48 publications

(19 citation statements)

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“…53 ff. where the total number of sounds is 3,000 in each text; the English is based on Hayden (1950). Consonants represent 48% of the total number of sounds in glossolalic Text I (as numbered by Wolfram), 55% in Text IV, and 63% in the English texts.…”

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confidence: 99%

“…of the total whereas alveolars represent 61.3'% and 60.9% respectively. These are compared with normal adult English where bilabials are said to make up 14.1 % of the consonants and alveolars 56.T% (Hayden, 1950, as interpreted by Wolfram, 1966). In both kinds of speech, therefore, bilabials make up less than 20% whereas apicals more than 50% of all consonants.…”

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confidence: 99%

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Glossolalia as Regressive Speech

Samarin

1973

Lang Speech

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Although there is considerable agreement about the phonological characteristics of religious glossolalia, based for the most part on utterances produced by native speakers of English, opinions differ widely about the causation of glossolalia. Most of the explanations are psychological or physiological. It is demonstrated that these explanations are inadequate because they are ad hoc and because they do not comprehend enough data. One hypothesis is treated in detail, namely, that glossolalia is a product of trance. This paper claims that a causative explanation for glossolalia must account for (1) religious glossolalic behaviour that is not demonstrably pathological or dissociative and (2) other spontaneous, ephemeral, and " meaningless " utterances in non-religious contexts.

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confidence: 99%

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confidence: 99%

Glossolalia as Regressive Speech

Samarin

1973

Lang Speech

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“…In all of the previous audioaero-tactile research, there was a bias toward /ba/ perception in the audio-only condition (Gick and Derrick, 2009;Gick et al, 2010;Derrick and Gick, 2013). On the other hand, /p/ (2.25% of phones) is more common than /b/ (1.65%) in American English speech (Hayden, 1950).…”

Section: Introductionmentioning

confidence: 83%

Visual-tactile integration in speech perception: Evidence for modality neutral speech primitives

Bicevskis

Derrick

Gick

2016

The Journal of the Acoustical Society of America

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Audio-visual [McGurk and MacDonald (1976). Nature 264, 746-748] and audio-tactile [Gick and Derrick (2009). Nature 462(7272), 502-504] speech stimuli enhance speech perception over audio stimuli alone. In addition, multimodal speech stimuli form an asymmetric window of integration that is consistent with the relative speeds of the various signals [Munhall, Gribble, Sacco, and Ward (1996). Percept. Psychophys. 58(3), 351-362; Gick, Ikegami, and Derrick (2010). J. Acoust. Soc. Am. 128(5), EL342-EL346]. In this experiment, participants were presented video of faces producing /pa/ and /ba/ syllables, both alone and with air puffs occurring synchronously and at different timings up to 300 ms before and after the stop release. Perceivers were asked to identify the syllable they perceived, and were more likely to respond that they perceived /pa/ when air puffs were present, with asymmetrical preference for puffs following the video signal-consistent with the relative speeds of visual and air puff signals. The results demonstrate that visual-tactile integration of speech perception occurs much as it does with audio-visual and audio-tactile stimuli. This finding contributes to the understanding of multimodal speech perception, lending support to the idea that speech is not perceived as an audio signal that is supplemented by information from other modes, but rather that primitives of speech perception are, in principle, modality neutral.

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“…It is known that the set of parameters, obtained from a database of naturally spoken utterances, comes from phoneme that has a different probability of occurrence. The probabilities have been studied, for example, in [ 12 ], where the most common phoneme of American English in the report was /ə/ with 9.96% of frequency, followed by /i/ with 9.75%. On the other hand, phonemes like /g/ and /h/ had a frequency of occurrence as low as 1.14% and 1.11% respectively.…”

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confidence: 99%

Discriminative Multi-Stream Postfilters Based on Deep Learning for Enhancing Statistical Parametric Speech Synthesis

Coto-Jiménez

2021

Biomimetics

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Statistical parametric speech synthesis based on Hidden Markov Models has been an important technique for the production of artificial voices, due to its ability to produce results with high intelligibility and sophisticated features such as voice conversion and accent modification with a small footprint, particularly for low-resource languages where deep learning-based techniques remain unexplored. Despite the progress, the quality of the results, mainly based on Hidden Markov Models (HMM) does not reach those of the predominant approaches, based on unit selection of speech segments of deep learning. One of the proposals to improve the quality of HMM-based speech has been incorporating postfiltering stages, which pretend to increase the quality while preserving the advantages of the process. In this paper, we present a new approach to postfiltering synthesized voices with the application of discriminative postfilters, with several long short-term memory (LSTM) deep neural networks. Our motivation stems from modeling specific mapping from synthesized to natural speech on those segments corresponding to voiced or unvoiced sounds, due to the different qualities of those sounds and how HMM-based voices can present distinct degradation on each one. The paper analyses the discriminative postfilters obtained using five voices, evaluated using three objective measures, Mel cepstral distance and subjective tests. The results indicate the advantages of the discriminative postilters in comparison with the HTS voice and the non-discriminative postfilters.

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The Relative Frequency of Phonemes in General-American English

Cited by 48 publications

References 1 publication

Glossolalia as Regressive Speech

Glossolalia as Regressive Speech

Visual-tactile integration in speech perception: Evidence for modality neutral speech primitives

Discriminative Multi-Stream Postfilters Based on Deep Learning for Enhancing Statistical Parametric Speech Synthesis

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