• Title/Summary/Keyword: Speaker normalization

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An Amplitude Warping Approach to Intra-Speaker Normalization for Speech Recognition (음성인식에서 화자 내 정규화를 위한 진폭 변경 방법)

  • Kim Dong-Hyun;Hong Kwang-Seok
    • Journal of Internet Computing and Services
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    • v.4 no.3
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    • pp.9-14
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    • 2003
  • The method of vocal tract normalization is a successful method for improving the accuracy of inter-speaker normalization. In this paper, we present an intra-speaker warping factor estimation based on pitch alteration utterance. The feature space distributions of untransformed speech from the pitch alteration utterance of intra-speaker would vary due to the acoustic differences of speech produced by glottis and vocal tract. The variation of utterance is two types: frequency and amplitude variation. The vocal tract normalization is frequency normalization among inter-speaker normalization methods. Therefore, we have to consider amplitude variation, and it may be possible to determine the amplitude warping factor by calculating the inverse ratio of input to reference pitch. k, the recognition results, the error rate is reduced from 0.4% to 2.3% for digit and word decoding.

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A New Power Spectrum Warping Approach to Speaker Warping (화자 정규화를 위한 새로운 파워 스펙트럼 Warping 방법)

  • 유일수;김동주;노용완;홍광석
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.4
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    • pp.103-111
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    • 2004
  • The method of speaker normalization has been known as the successful method for improving the accuracy of speech recognition at speaker independent speech recognition system. A frequency warping approach is widely used method based on maximum likelihood for speaker normalization. This paper propose a new power spectrum warping approach to making improvement of speaker normalization better than a frequency warping. Th power spectrum warping uses Mel-frequency cepstrum analysis(MFCC) and is a simple mechanism to performing speaker normalization by modifying the power spectrum of Mel filter bank in MFCC. Also, this paper propose the hybrid VTN combined the Power spectrum warping and a frequency warping. Experiment of this paper did a comparative analysis about the recognition performance of the SKKU PBW DB applied each speaker normalization approach on baseline system. The experiment results have shown that a frequency warping is 2.06%, the power spectrum is 3.06%, and hybrid VTN is 4.07% word error rate reduction as of word recognition performance of baseline system.

Quantization Based Speaker Normalization for DHMM Speech Recognition System (DHMM 음성 인식 시스템을 위한 양자화 기반의 화자 정규화)

  • 신옥근
    • The Journal of the Acoustical Society of Korea
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    • v.22 no.4
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    • pp.299-307
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    • 2003
  • There have been many studies on speaker normalization which aims to minimize the effects of speaker's vocal tract length on the recognition performance of the speaker independent speech recognition system. In this paper, we propose a simple vector quantizer based linear warping speaker normalization method based on the observation that the vector quantizer can be successfully used for speaker verification. For this purpose, we firstly generate an optimal codebook which will be used as the basis of the speaker normalization, and then the warping factor of the unknown speaker will be extracted by comparing the feature vectors and the codebook. Finally, the extracted warping factor is used to linearly warp the Mel scale filter bank adopted in the course of MFCC calculation. To test the performance of the proposed method, a series of recognition experiments are conducted on discrete HMM with thirteen mono-syllabic Korean number utterances. The results showed that about 29% of word error rate can be reduced, and that the proposed warping factor extraction method is useful due to its simplicity compared to other line search warping methods.

Statistical Approaches to Convert Pitch Contour Based on Korean Prosodic Phrases (한국어 운율구 기반의 피치궤적 변환의 통계적 접근)

  • Lee, Ki-Young
    • The Journal of the Acoustical Society of Korea
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    • v.23 no.1E
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    • pp.10-15
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    • 2004
  • In performing speech conversion from a source speaker to a target speaker, it is important that the pitch contour of the source speakers utterance be converted into that of the target speaker, because pitch contour of a speech utterance plays an important role in expressing speaker's individuality and meaning of the utterance. This paper describes statistical algorithms of pitch contour conversion for Korean language. Pitch contour conversions are investigated at two 1 evels of prosodic phrases: intonational phrase and accentual phrase. The basic algorithm is a Gaussian normalization [7] in intonational phrase. The first presented algorithm is combined with a declination-line of pitch contour in an intonational phrase. The second one is Gaussian normalization within accentual phrases to compensate for local pitch variations. Experimental results show that the algorithm of Gaussian normalization within accentual phrases is significantly more accurate than the other two algorithms in intonational phrase.

Speaker Change Detection by Normalization of Phonetic Characteristics (음소 특성 정규화를 통한 화자 변화 검출)

  • Kim Hyung Soon;Park Hae Young;Park Sun Young
    • MALSORI
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    • no.47
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    • pp.97-107
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    • 2003
  • Speaker change detection is to detect automatically a point of time at which speaker was replaced. Since feature parameters used for speaker change detection depend not only on speaker characteristics but also on phonetic characteristics, spoken contents included in the feature parameters inevitably causes performance degradation of speaker change detection. In this paper, to alleviate this problem, a method to normalize phonetic variations in speech feature parameters is proposed for emphasizing changes due to speaker characteristics. Experimental results show that the proposed method improves the performance of speaker change detection.

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Spectral Normalization for Speaker-Invariant Feature Extraction (화자 불변 특징추출을 위한 스펙트럼 정규화)

  • 오광철
    • Proceedings of the Acoustical Society of Korea Conference
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    • 1993.06a
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    • pp.238-241
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    • 1993
  • We present a new method to normalize spectral variations of different speakers based on physiological studies of hearing. The proposed method uses the cochlear frequency map to warp the input speech spectra by interpolation or decimation. Using this normalization method, we can obtain much improved recognition results for speaker independent speech recognition.

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Speaker Normalization using Gaussian Mixture Model for Speaker Independent Speech Recognition (화자독립 음성인식을 위한 GMM 기반 화자 정규화)

  • Shin, Ok-Keun
    • The KIPS Transactions:PartB
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    • v.12B no.4 s.100
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    • pp.437-442
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    • 2005
  • For the purpose of speaker normalization in speaker independent speech recognition systems, experiments are conducted on a method based on Gaussian mixture model(GMM). The method, which is an improvement of the previous study based on vector quantizer, consists of modeling the probability distribution of canonical feature vectors by a GMM with an appropriate number of clusters, and of estimating the warp factor of a test speaker by making use of the obtained probabilistic model. The purpose of this study is twofold: improving the existing ML based methods, and comparing the performance of what is called 'soft decision' method with that of the previous study based on vector quantizer. The effectiveness of the proposed method is investigated by recognition experiments on the TIMIT corpus. The experimental results showed that a little improvement could be obtained tv adjusting the number of clusters in GMM appropriately.

Pitch Contour Conversion Using Slanted Gaussian Normalization Based on Accentual Phrases

  • Lee, Ki-Young;Bae, Myung-Jin;Lee, Ho-Young;Kim, Jong-Kuk
    • Speech Sciences
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    • v.11 no.1
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    • pp.31-42
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    • 2004
  • This paper presents methods using Gaussian normalization for converting pitch contours based on prosodic phrases along with experimental tests on the Korean database of 16 declarative sentences and the first sentences of the story of 'The Three Little Pigs'. We propose a new conversion method using Gaussian normalization to the pitch deviation of pitch contour subtracted by partial declination lines: by using partial declination lines for each accentual phrase of pitch contour, we avoid the problem that a Gaussian normalization using average values and standard deviations of intonational phrase tends to lose individual local variability and thus cannot modify individual characteristics of pitch contour from a source speaker to a target speaker. From the results of the experiments, we show that this slanted Gaussian normalization using these declination lines subtracted from pitch contour of accentual phrases can modify pitch contour more accurately than other methods using Gaussian normalization.

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Histogram Equalization Using Background Speakers' Utterances for Speaker Identification (화자 식별에서의 배경화자데이터를 이용한 히스토그램 등화 기법)

  • Kim, Myung-Jae;Yang, Il-Ho;So, Byung-Min;Kim, Min-Seok;Yu, Ha-Jin
    • Phonetics and Speech Sciences
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    • v.4 no.2
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    • pp.79-86
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    • 2012
  • In this paper, we propose a novel approach to improve histogram equalization for speaker identification. Our method collects all speech features of UBM training data to make a reference distribution. The ranks of the feature vectors are calculated in the sorted list of the collection of the UBM training data and the test data. We use the ranks to perform order-based histogram equalization. The proposed method improves the accuracy of the speaker recognition system with short utterances. We use four kinds of speech databases to evaluate the proposed speaker recognition system and compare the system with cepstral mean normalization (CMN), mean and variance normalization (MVN), and histogram equalization (HEQ). Our system reduced the relative error rate by 33.3% from the baseline system.

Histogram Equalization Using Centroids of Fuzzy C-Means of Background Speakers' Utterances for Majority Voting Based Speaker Identification (다수 투표 기반의 화자 식별을 위한 배경 화자 데이터의 퍼지 C-Means 중심을 이용한 히스토그램 등화기법)

  • Kim, Myung-Jae;Yang, Il-Ho;Yu, Ha-Jin
    • The Journal of the Acoustical Society of Korea
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    • v.33 no.1
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    • pp.68-74
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    • 2014
  • In a previous work, we proposed a novel approach of histogram equalization using a supplement set which is composed of centroids of Fuzzy C-Means of the background utterances. The performance of the proposed method is affected by the size of the supplement set, but it is difficult to find the best size at the point of recognition. In this paper, we propose a histogram equalization using a supplement set for majority voting based speaker identification. The proposed method identifies test utterances using a majority voting on the histogram equalization methods with various sizes of supplement sets. The proposed method is compared with the conventional feature normalization methods such as CMN(Cepstral Mean Normalization), MVN(Mean and Variance Normalization), and HEQ(Histogram Equalization) and the histogram equalization method using a supplement set.