Acoustic characteristics of speaker individuality: Control and conversion

Kuwabara, Hisao; Sagisaka, Yoshinori

doi:10.1016/0167-6393(94)00053-d

Cited by 106 publications

(50 citation statements)

References 30 publications

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“…The importance of the fundamental frequency in speaker identification is reported in many studies, including the studies on automatic speaker recognition [17,18]. Hashimoto et al [31] reported that the average fundamental frequency is the most effective parameter in unknown speaker identification.…”

Section: Introductionmentioning

confidence: 99%

“…Formant frequencies of the vowels [13][14][15], long-term average speech spectra [16] and average pitch frequency are said to indicate speakers' individuality [15,17,18], just to name a few. O'Shaughnessy [19] suggests that one way to find the acoustic parameters that indicate the speaker individuality is to conduct perceptual speaker identification tests where various kinds of sounds are examined.…”

Section: Introductionmentioning

confidence: 99%

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Effects of linguistic contents on perceptual speaker identification: Comparison of familiar and unknown speaker identifications

Amino

Arai

2009

Acoust. Sci. & Tech.

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There are several factors that affect human speaker recognition. In this study, two experiments were conducted in order to see the effects that the stimulus contents and the familiarity to the speakers give to the perception of the speakers. The results showed that: a) stimuli including a nasal were effective for accurate speaker identification; b) coronal nasals were more effective than the labial nasal, and c) the familiarity to the speakers gives a great influence on the performance. The tendencies a) and b) were observed both in familiar and unknown speaker identifications. The results of the acoustical analyses also showed that there were correspondences between the perception of the speaker identity and the cepstral distances among the speakers. The inter-speaker cepstral distances were greater in vowel intervals than in the consonant intervals; especially, notably they were greater in nasals than in orals in the consonant intervals.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of linguistic contents on perceptual speaker identification: Comparison of familiar and unknown speaker identifications

Amino

Arai

2009

Acoust. Sci. & Tech.

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“…The intonation of DQs and statements in Indonesian language are subject to change from various reasons [13][14][15][16]. A speaker can utter a word of Indonesian with different stressed syllables without changing its meaning [12,[17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Automatic recognition of Indonesian declarative questions and statements using polynomial coefficients of the pitch contours

Effendy

Sairyo

Furui

et al. 2009

Acoust. Sci. & Tech.

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Abstract:We propose an automatic utterance type recognizer that distinguishes declarative questions from statements in Indonesian speech. Since utterances in these two types have the same words with the same order and differ only in their intonations, their classification requires not only a speech recognizer, but also an intonation recognizer. In this paper, the most important utterance part for distinguishing those two types is first identified by perceptual experiments. Then, an utterance type recognizer using that part is proposed, where polynomial expansion is used as a feature extractor and a neural network is used as a classifier. We evaluated our method using Indonesian speech database including 29 pairs of sentences of those two types, each of which uttered by 35 speakers. It was proved that final word and final-two-syllables are equally effective for the discrimination of each utterance. The proposed recognizer achieved the best accuracy of 89.1% when the order of polynomial expansion was three and the neural network was a linear perceptron.

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“…Fundamental frequency (F0) is the most complex acoustic cue, being related in many languages to vowel quality. All these formants can play an important role in speaker identification to different extents [43,44].…”

Section: Vowel Database Constructionmentioning

confidence: 99%

Text-Independent Speaker Identification Using Vowel Formants

Almaadeed

Aggoun

Amira

2015

J Sign Process Syst

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Automatic speaker identification has become a challenging research problem due to its wide variety of applications. Neural networks and audio-visual identification systems can be very powerful, but they have limitations related to the number of speakers. The performance drops gradually as more and more users are registered with the system. This paper proposes a scalable algorithm for real-time text-independent speaker identification based on vowel recognition. Vowel formants are unique across different speakers and reflect the vocal tract information of a particular speaker. The contribution of this paper is the design of a scalable system based on vowel formant filters and a scoring scheme for classification of an unseen instance. Mel-Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) have both been analysed for comparison to extract vowel formants by windowing the given signal. All formants are filtered by known formant frequencies to separate the vowel formants for further processing. The formant frequencies of each speaker are collected during the training phase. A test signal is also processed in the same way to find vowel formants and compare them with the saved vowel formants to identify the speaker for the current signal. A score-based scheme allows the speaker with the highest matching formants to own the current signal. This model requires less than 100 bytes of data to be saved for each speaker to be identified, and can identify the speaker within a second. Tests conducted on multiple databases show that this score-based scheme outperforms the back propagation neural network and Gaussian mixture models. Usually, the longer the speech files, the more significant were the improvements in accuracy.

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Acoustic characteristics of speaker individuality: Control and conversion

Cited by 106 publications

References 30 publications

Effects of linguistic contents on perceptual speaker identification: Comparison of familiar and unknown speaker identifications

Effects of linguistic contents on perceptual speaker identification: Comparison of familiar and unknown speaker identifications

Automatic recognition of Indonesian declarative questions and statements using polynomial coefficients of the pitch contours

Text-Independent Speaker Identification Using Vowel Formants

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