Exploiting High-Level Information Provided by ALISP in Speaker Recognition

Hannani, Asmaa El; Petrovska‐Delacrétaz, Dijana

doi:10.1007/11613107_4

Cited by 8 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ps (si sj~i ... si-) and a left-to-right HMM having three emitting states and con-PBM (Si |3i±1+ *. *3*j1) the probabilities that the segtamning up to 8 For the ALISP based systems, the speech parameteri- shown that like phonetic systems, the data-driven systems can complement short-term acoustic system to reach better speaker recognition performances. Indeed, the fusion Figure 1 shows DET curves of the fusion results of the of the acoustic GMM system with the ALISP systems suphigh-level data-driven systems.…”

Section: Complementary Information One Set Of Information Reflectsmentioning

confidence: 99%

“…This way the availability of corpora is on the NIST 2006 Speaker Recognition Evaluation data much less an issue and the training corpus can be chosen to show that the data-driven features provide complementary match the working conditions as much as possible. information and the resulting fused system reduced the erThis paper is the continuation of previous attempts ror rate in comparison to the GMM baseline system.to model high-level information using data-driven approaches [9,8]. The focus here is on the fusion of different systems that exploit data-driven high-level source of in-…”

mentioning

confidence: 96%

“…to model high-level information using data-driven approaches [9,8]. The focus here is on the fusion of different systems that exploit data-driven high-level source of in-…”

mentioning

confidence: 99%

See 2 more Smart Citations

Data-Driven High-Level Information for Text-Independent Speaker Verification

Hannani¹,

Petrovska‐Delacrétaz²

2007

2007 IEEE Workshop on Automatic Identification Advanced Technologies

Self Cite

View full text Add to dashboard Cite

only the acoustic content of speech to trying to exploit highlevel information. It has been reported in several studies that Recently, various studies have shown that high-levelfeagains in speaker recognition accuracy are possible by extures, such as linguistic content, pronunciation and idiolecploiting such high-level information sources (see e.g.[17]). tal word usage, convey more speaker information and can The most examined high-level information for speaker be added to the low-level features in order to increase the verification are: the prosody [19, 1], the phonetic informarobustness of the system. Usually these features are extion [15,14,13,11], and the idiolectal word and phone tracted by analyzing streams produced by phonetic speech usage [7, 2, 5, 12]. All these approaches reported encourrecognition systems. Two of the major problems that arise aging results and were found to provide features complewhen phone based systems are being developed are the posmentary to short-term acoustic features. However most of sible mismatches between the development and evaluation them are based on phonetic transcriptions that are errordata and the lack of transcribed databases. We propose in prone and expensive to create. Beside this, the transcribed this paper to replace the phone-based approaches by datadatabases need also to be updated with new data sets in ordriven segmentation methodologies. Our data-driven highder to match with potentially new specifications (channel, level systems do not use transcribed data and can easily be microphones, context of use, ...) of the verification data.applied on development data minimizing the mismatches. An alternative approach that solves these two problems is These systems were fused with a state-of-the-art acoustic using data-driven phone-like units derived directly from unGaussian Mixture Models (GMM) system. Results obtained transcribed speech. This way the availability of corpora is on the NIST 2006 Speaker Recognition Evaluation data much less an issue and the training corpus can be chosen to show that the data-driven features provide complementary match the working conditions as much as possible. information and the resulting fused system reduced the erThis paper is the continuation of previous attempts ror rate in comparison to the GMM baseline system.to model high-level information using data-driven approaches [9,8]. The focus here is on the fusion of different systems that exploit data-driven high-level source of in-

show abstract

Section: Complementary Information One Set Of Information Reflectsmentioning

confidence: 99%

mentioning

confidence: 96%

See 1 more Smart Citation

Data-Driven High-Level Information for Text-Independent Speaker Verification

Hannani¹,

Petrovska‐Delacrétaz²

2007

2007 IEEE Workshop on Automatic Identification Advanced Technologies

Self Cite

View full text Add to dashboard Cite

show abstract

“…For fusion of the classifiers, a Multi Layer Perceptron (MLP) was used (which has successfully been applied before, e.g., El Hannani and Petrovska-Delcretaz [60], Campbell et al [32]). This popular type of feedforward neural network consists of an input layer (the input features), possibly several hidden layers of neurons and an output layer.…”

Section: Multi Layer Perceptron (Mlp)mentioning

confidence: 99%

How does real affect affect affect recognition in speech?

Truong¹

View full text Add to dashboard Cite

“…In [4] we presented a similar system to [3] but we used the automatic segmentation based on Automatic Language Independent Speech Processing (ALISP) tools [5] instead of the phonetic one. In this system, speaker specific information is captured only by analyzing sequences of ALISP units.…”

Section: Introductionmentioning

confidence: 99%

Using Data-driven and Phonetic Units for Speaker Verification

Hannani

Toledano

Petrovska‐Delacrétaz³

et al. 2006

2006 IEEE Odyssey - The Speaker and Language Recognition Workshop

View full text Add to dashboard Cite

Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: AbstractRecognition of speaker identity based on modeling the streams produced by phonetic decoders (phonetic speaker recognition) has gained popularity during the past few years. Two of the major problems that arise when phone based systems are being developed are the possible mismatches between the development and evaluation data and the lack of transcribed databases. Data-driven segmentation techniques provide a potential solution to these problems because they do not use transcribed data and can easily be applied on development data minimizing the mismatches. In this paper we compare speaker recognition results using phonetic and data-driven decoders. To this end, we have compared the results obtained with a speaker recognition system based on data-driven acoustic units and phonetic speaker recognition systems trained on Spanish and English data. Results obtained on the NIST 2005 Speaker Recognition Evaluation data show that the data-driven approach outperforms the phonetic one and that further improvements can be achieved by combining both approaches.

show abstract

Exploiting High-Level Information Provided by ALISP in Speaker Recognition

Cited by 8 publications

References 8 publications

Data-Driven High-Level Information for Text-Independent Speaker Verification

Data-Driven High-Level Information for Text-Independent Speaker Verification

How does real affect affect affect recognition in speech?

Using Data-driven and Phonetic Units for Speaker Verification

Contact Info

Product

Resources

About