Text-independent Speaker Verification

Hannani, Asmaa El; Petrovska‐Delacrétaz, Dijana; Fauve, Benoit; Mayoue, Aurélien; Mason, John S.; Bonastre, Jean-François; Chollet, G.

doi:10.1007/978-1-84800-292-0_7

Cited by 15 publications

(7 citation statements)

References 57 publications

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“…As explained in [9,11,13,14,21], and shown in Fig. 2, the set of ALISP units is automatically acquired through parametrization, temporal decomposition, vector quantization, and Hidden Markov Modeling.…”

Section: The Acquisition and Modeling Of Alisp Unitsmentioning

confidence: 99%

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Automatic detection of known advertisements in radio broadcast with data-driven ALISP transcriptions

Khemiri

Chollet

Petrovska‐Delacrétaz

2011

Multimed Tools Appl

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This paper presents an audio indexing system to search for known advertisements in radio broadcast streams, using automatically acquired segmental units. These units, called Automatic Language Independent Speech Processing (ALISP) units, are acquired using temporal decomposition and vector quantization and modeled by Hidden Markov Models (HMMs). To detect commercials, ALISP transcriptions of reference advertisements are compared to the transcriptions of the test radio stream using the Levenshtein distance. The system is described and evaluated on one day broadcast audio streams from 11 French radio stations containing 2070 advertisements. With a set of 2,172 reference advertisements we achieve a mean precision rate of 99% with the corresponding recall value of 96%. Moreover, this system allowed us to detect some annotation errors.

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Section: The Acquisition and Modeling Of Alisp Unitsmentioning

confidence: 99%

“…This is the originality of the ALISP approach. ALISP is a data-driven technique that was first developed for very low bit-rate speech coding [10], and then successfully adapted for other tasks such as speaker [14] and language recognition [11]. Schwarz [23] showed that data-driven approaches are also applicable for music signals.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of known advertisements in radio broadcast with data-driven ALISP transcriptions

Khemiri

Chollet

Petrovska‐Delacrétaz

2011

Multimed Tools Appl

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“…This system was used to identify music titles in a radio broadcast stream. ALISP (Automatic Language Independent Speech Processing) [6] is a data-driven technique that was first developed for very low bit-rate speech coding [5], and then successfully adapted for other tasks such as speaker [9] and language recognition [22]. The advantage of ALISP tools is that no manual transcriptions are needed in the training phase and they can be easily deployed on new data, tasks, or languages.…”

Section: Introductionmentioning

confidence: 99%

Detection of repeating items in audio streams using data-driven ALISP sequencing

Khemiri

Petrovska‐Delacrétaz

Chollet

2014

2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

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Radio streams often contain redundant parts. Commercials on radio or television stations, songs on music channels and jingles broadcasted before a specific radio or TV show, are some of the repeating objects in multimedia streams. In this paper, an audio fingerprinting system to detect repeating objects in audio streams is proposed. In order to resolve this problem, the ARGOS segmentation framework is used. This framework is combined with the ALISP-based audio fingerprinting system to build a new audio motif detection system. An approximate string matching algorithm inspired from BLAST technique is applied to speed up the approximate string matching to find the repeating items in the audio streams. Most of the audio motif discovery systems proposed in the literature are evaluated on repeating songs with long duration (about 5min). In our case, the ALISPbased system is evaluated on advertisements and songs where the duration could vary from few seconds to some minutes. The system is evaluated on 21 days from 3 French radio stations. On a set of 3081 repeating songs and 1315 repeating advertisements a mean recall rate of 98% with the corresponding precision value of 99% were achieved. The results show that the system is robust against different kinds of distortions present in radio streams.

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“…This is one of the originalities of the AL-ISP approach (Automatic Language Independent Speech Processing). ALISP tools were first developed for very low bit-rate speech coding [6], and then were successfully adapted for other tasks such as speaker [8] and language recognition [7]. Schwarz [15] showed that data-driven approaches are also applicable for music signals.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of known advertisements in radio broadcast with data-driven ALISP transcriptions

Khemiri¹,

Chollet²,

Petrovska-Delacretaz³

2011

2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI)

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International audienceThis paper describes an audio indexing system to search for known advertisements in radio broadcast streams, using automatically acquired segmental units. These segmental units called ALISP units are acquired automatically using temporal decomposition and vector quantization and modeled by Hidden Markov Models (HMMs). To detect commercials, ALISP transcriptions of reference advertisements are compared to those of radio stream using the Leven-shtein distance. The system is described and evaluated using broadcast streams provided by YACAST. On a set of 802 advertisements we achieve a mean precision of 95% with the corresponding recall value of 97%. The results show that the system is robust in situations where the advertisement to detect is stretched or suffer from time distortions. Moreover, this system allowed us to detect some annotation error

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Text-independent Speaker Verification

Cited by 15 publications

References 57 publications

Automatic detection of known advertisements in radio broadcast with data-driven ALISP transcriptions

Automatic detection of known advertisements in radio broadcast with data-driven ALISP transcriptions

Detection of repeating items in audio streams using data-driven ALISP sequencing

Automatic detection of known advertisements in radio broadcast with data-driven ALISP transcriptions

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