S. Chen and P. Gopalakrishnan, "Speaker, environment and channel change detection and clustering via the bayesian information criterion," Proc. Broadcast News Trans. and Under. Workshop , 1998.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....of these acoustic changes will correspond to speaker turns. The second step is used then to validate or discard these possible turns. The techniques used for the first step can be classified into three different groups: phone decoding [3] hypothesis testing [4] and distance based segmentation [5, 6]. Distance based segmentation approaches have proved to be more robust for non collaborative speaker segmentation, and thus, in this paper we will use an algorithm called DISTBIC (see [6] for details) to segment the audio data. DISTBIC is also a two step segmentation technique, which is inspired ....

....have proved to be more robust for non collaborative speaker segmentation, and thus, in this paper we will use an algorithm called DISTBIC (see [6] for details) to segment the audio data. DISTBIC is also a two step segmentation technique, which is inspired on the BIC algorithm developed by IBM [5]. In the first step the distance between adjacent windows is obtained every 100ms. This result in a distance signal d(t) In our implementation we use the symmetrical KullbackLeibler distance [7] Then, the significant peaks of d(t) are considered as turn candidates. In the second step the turn ....

[Article contains additional citation context not shown here]

S. S. Chen and P. S. Gopalakrishnan, "Speaker environment and channel change detection and clustering via de bayesian information criterion," in DARPA Speech Recognition Workshop, Landsdowne, VA, Feb. 1998, pp. 127--132.

....d(t 2) Figure 1. Sliding windows to speaker turns. The second step is used then to validate or discard these possible turns. The techniques used for the first step can be classified into three different groups: phone decoding ( 7, 8] hypothesis testing ( 14] and distancebased segmentation ([12, 4, 5]) Distance based segmentation approaches have proved to be more robust for noncollaborative speaker segmentation, and thus, in this paper we will use an algorithm called DISTBIC (see [5] for details) to segment the audio data. DISTBIC is also a twostep segmentation technique, which is inspired on ....

....have proved to be more robust for noncollaborative speaker segmentation, and thus, in this paper we will use an algorithm called DISTBIC (see [5] for details) to segment the audio data. DISTBIC is also a twostep segmentation technique, which is inspired on the BIC algorithm developed by IBM ([4]) In the first step the distance between adjacent windows is obtained every 100ms. This result in a distance signal d(t) see Figure 1. In our implementation we use the symmetrical Kullback Leibler [12] distance. The significant peaks of d(t) are considered as turn candidates. In the second step ....

[Article contains additional citation context not shown here]

S. S. Chen and P. S. Gopalakrishnan. Speaker environment and channel change detection and clustering via de bayesian information criterion. In DARPA Speech Recognition Workshop, pages 127--132, Landsdowne, VA, February 1998.

....adjacent windows is obtained every 100ms yielding in a distance signal . In our implementation we use the symmetrical kullback Leibler [4] distance. The significant peaks of 339 are considered as turn candidates. In the second step the turn candidates are validated using the criteria [7]. To that end, the acoustic vectors of adjacent segments are modeled separately using Gaussian models. The model of the union of the acoustic vectors of both segments is also computed and then the criteria is used to check is the likelihood of the union is greater than the likelihood of both ....

S. S. Chen and P. S. Gopalakrishnan, "Speaker environment and channel change detection and clustering via de bayesian information criterion," in DARPA Speech Recognition Workshop, 1998.

....are more clusters than speakers, as a cluster can represent a speaker in a given acoustic environment. The second measure is the cluster purity, defined as the percentage of frames in the given cluster associated with the most represented speaker in the cluster. A similar measure was proposed in [1], but at the segment level. The table shows the weighted average cluster purities for the 4 shows. On average 96 of the data in a cluster comes from a single speaker. When clusters are impure, they tend to include speakers with similar acoustic conditions. The best cluster coverage is a ....

S.S. Chen, P.S. Gopalakrishnan, "Speaker, Environment and Channel Change Detection and Clustering via the Bayesian Information Criterion", DARPA Broadcast News Transcription and Understanding Workshop, pp. 127-132, February 1998.

....impact on errors because if a detected boundary is somewhat displaced and is outside the tolerance window two errors will occur: a deletion and an insertion. This segmentation system is sufficiently accurate and at the same time much less computational intensive than for instance the more used BIC [2, 3] that evaluates three full covariance matrices at each time frame. 3. SPEECH NON SPEECH DISCRIMINATION After the acoustic segmentation stage each segment is classified using a speech non speech discriminator, tagging audio portions without speech, with too much noise or pure music. This ....

....and the two closer ones are considered for joining in a new cluster. Clusters are linked together until the distances exceed a pre defined value. At that point the clustering ends. Several appropriate distance measures can be used, namely the KL2 [1] the generalized likelihood ratio or the BIC [2, 3]. Our first experiments were conducted using the KL2 metrics to evaluate cluster distances. Latter on, we developed a more efficient distance measure based on the BIC. The distance measure when comparing two clusters using the BIC can be stated as a model selection criterion where one model is ....

S. Chen and P. S. Gopalakrishnan, "Speaker, environment and channel change detection and clustering via the bayesian information criterion," in DARPA Proc. Speech Recognition Workshop, 1998.

....identification. Our speaker clustering algorithm makes use of gender detection. Speech segments with different gender classification are clustered separately. We used bottom up hierarchical clustering [4] We developed an efficient distance measure based on the Bayesian Information Criterion (BIC) [7, 8]. An adjacency term is used instead of the BIC threshold [6] Empirically clusters having adjacent speech segments are closer in time and the probability of belonging to the same speaker must be higher. Using this we obtained a cluster purity greater than 97 with a mean number of clusters per ....

S. Chen and P. S. Gopalakrishnan, "Speaker, environment and channel change detection and clustering via the bayesian information criterion, " in DARPA Proc. Speech Recognition Workshop, 1998.

....step produces segments that contain more than one speaker then the speaker models will be estimated incorrectly. Therefore, we are investigating into a new speaker change detection method. Two approaches commonly used for speaker change detection are energy based [1] 2] and distance based [3][4][5] and. The second approach [1] 2] assumes that the probability of a speaker change is higher around silence regions. It uses speech silence detector to identify the speaker change locations. The distance based method searches for the speaker change candidates at the maxima of the distances ....

....label speech (CLD) and the total amount of speech (TS) 1 CLD TS. Since overlapped speech regions belong to both speakers, they are not taken into account in the scoring process. 4 . SYSTEM DESCRIPTION Most of the speaker segmentation systems use Melfrequency Cepstral coefficients [1] 2] 3][4][5] However, in previous experiments using the development database, Line Spectral Pair has shown around 20 improvement over the Mel frequency Cepstral coefficients. Therefore we use 24 LSP [8] coefficients as features for both systems. They are computed every 10ms using a 32ms Hamming window. ....

S. Chen, P. Gopalakrishnan "Speaker, environment and channel change detection and clustering via the Bayesian Information Criterion", DARPA Broadcast News Transcription and Understanding Workshop, Landsdowne, VA, 1998.

....television broadcast news, a small part of which has been used in this work. According to our plans, by the end of this year about 100 hours of transcribed material will be available for development and evaluation purposes. 3. SEGMENTATION AND CLUSTERING The Bayesian Information Criterion (BIC) [2] is applied to segment the input audio stream into acoustically homogeneous chunks. Gaussians mixture models are then used to classify segments in terms of acoustic source and channel. Emission probability densities consist of mixtures of European Language Resources Association. ....

....matrices. Observations are 39 dimension vectors (see Section 4.1) Six classes are considered for classification: female male wide band speech, female male narrowband speech, pure music, and silence plus other non speech events. Clustering of speech segments is done by a bottom up scheme [2, 3] that groups segments which are acoustically close with respect to the BIC. As a result, this step should gather segments of the same speaker. To evaluate the segmentation algorithm in detecting the break points, recall and precision are computed with respect to target (manually annotated) ....

Chen, S. S. and Gopalakrishnan, P. S., "Speaker, Environment and Channel Change Detection and Clustering via the Bayesian Information Criterion", in Proc. of the DARPA Broadcast News Transcription and Understanding Workshop, Lansdowne, VA, 1998.

....as follows: Non speech passages were eliminated using a Gaussian Mixture Model (GMM) decoder that recognizes speech and non speech. Subsequently, the passages of speech are divided at changes in speaker or background conditions using the Bayesian Information Criterion (BIC) as de scribed in [Chen 1998]. The segmentation used in the 1997 HUB4 evaluation was based on using gender dependent phone decoders (PHONE DEC. with additional non speech units (see [Beyerlein 1998] approach WER ( PHONE DEC. SNN (1997) 22.6 GMM BIC bottom up (1998) 21.0 NIST PE ideal cl. 20.0 Table 2: Word ....

S.S. Chen, P.S. Gopalakrishnan, "Speaker, Environment and Channel Change Detection and Clustering via the Bayesian Information Criterion", in Proc. DARPA Broadcast News Transcription and Understanding Workshop, VA, Feb. 1998.

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S. Chen and P. Gopalakrishnan, "Speaker, environment and channel change detection and clustering via the bayesian information criterion," Proc. Broadcast News Trans. and Under. Workshop , 1998.

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S. S. Chen and P.S. Gopalakrishnan, "Speaker, Environment and Channel Change Detection and Clustering Via the Bayesian Information Criterion," Proc., DARPA Workshop, 1998, pp. 127--132.

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S.S. Chen and P.S. Gopalakrishnan, "Speaker, Environment and Channel Change Detection and Clustering via the Bayesian Information Criterion," Proceedings of DARPA Landsdowne,VA, Feb. 1998.

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S. S. Chen and P. S. Gopalakrishnan, "Speaker Environment And Channel Change Detection And Clustering Via The Bayesian Information Criterion," in DARPA Speech Recognition Workshop Proc., 1998.

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S. S. Chen and P. S. Gopalakrishnan, "Speaker environment and channel change detection and clustering via de bayesian information criterion," in DARPA Speech Recognition Workshop, Landsdowne, VA, Feb. 1998, pp. 127--132.

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S. S. Chen and P. S. Gopalakrishnan, \Speaker environment and channel change detection and clustering via de bayesian information criterion," in DARPA Speech Recognition Workshop, 1998.

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S. Chen and P. S. Gopalakrishnan, "Speaker, environment and channel change detection and clustering via the Bayesian information criterion," in Proc. DARPA Broadcast News Transcription and Understanding Workshop, 1998.

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S. S. Chen and P. S. Gopalakrishnan, "Speaker, environment and channel change detection and clustering via the Bayesian information criterion," Tech. Rep., IBM T.J. Watson Research Center, 1998.

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S. Chen, P. Gopalakrishnan, "Speaker, environmentand channel change detection and clustering via the Bayesian Information Criterion", Proc. DARPA Speech Recognition Workshop pp.127-132 (1998)

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S. Chen and P. Gopalakrishnan, "Speaker, environment and channel change detection and clustering via the bayesian information criterion," Proc. Broadcast News Trans. and Under. Workshop , 1998.

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S. Chen, P. Gopalakrishnan, "Speaker, Environment and Channel Change Detection and Clustering via The Bayesian Information Criterion," Proc. Broadcast News Trans. & Under. Workshop, 1998.

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S. S. Chen and P. S. Gopalkrishnan, "Speaker, environment and channel change detection and clustering via the bayesian information criterion," IBM Technical Journal, 1998.

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S. S. Chen and P. S. Gopalakrishnan, "Speaker, environment and channel change detection and clustering via the Bayesian information criterion," Tech. Rep., IBM T.J. Watson Research Center, 1998.

No context found.

S. S. Chen and P. S. Gopalakrishnan, "Speaker, environment and channel change detection and clustering via the Bayesian information criterion," Tech. Rep., IBM T.J. Watson Research Center, 1998.

No context found.

S. Chen and P. S. Gopalakrishnan, "Speaker, environment and channel change detection and clustering via the bayesian information criterion," in Proceedings of Broadcast News Transcription and Understanding Workshop, Feb 1998.

No context found.

S. S. Chen and P. S. Gopalakrishnan, "Speaker, environment and channel change detection and clustering via the Bayesian information criterion," Tech. Rep., IBM T.J. Watson Research Center, 1998.

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