Mohamed Bouallegue

Improving dialogue classification using a topic space representation and a Gaussian classifier based on the decision rule

In this paper, we study the impact of dialogue representations and classification methods in the task of theme identification of telephone conversation services having highly imperfect automatic transcriptions. Two dialogue representations are firstly compared: the classical Term Frequency-Inverse Document Frequency with Gini purity criteria (TF-IDF-Gini) method and the Latent Dirichlet Allocation (LDA) approach. We then propose to study an original classification method that takes advantage of the LDA topic space representation, highlighted as the best dialogue representation. To do so, two assumptions about topic representation led us to choose a Gaussian process (GP) based method. This approach is compared with a Support Vector Machine (SVM) classification method. Results show that the GP approach is a better solution to deal with the multiple theme complexity of a dialogue, no matter the conditions studied (manual or automatic transcriptions). We finally discuss the impact of the topic space reduction on the classification accuracy.

An I-vector Based Approach to Compact Multi-Granularity Topic Spaces Representation of Textual Documents

Various studies highlighted that topicbased approaches give a powerful spoken content representation of documents. Nonetheless, these documents may contain more than one main theme, and their automatic transcription inevitably contains errors. In this study, we propose an original and promising framework based on a compact representation of a textual document, to solve issues related to topic space granularity. Firstly, various topic spaces are estimated with different numbers of classes from a Latent Dirichlet Allocation. Then, this multiple topic space representation is compacted into an elementary segment, calledc-vector, originally developed in the context of speaker recognition. Experiments are conducted on the DECODA corpus of conversations. Results show the effectiveness of the proposed multi-view compact representation paradigm. Our identification system reaches an accuracy of 85%, with a significant gain of 9 points compared to the baseline (best single topic space configuration).

A simplified Subspace Gaussian Mixture to compact acoustic models for speech recognition

Speech recognition applications are known to require a significant amount of resources (memory, computing power). However, embedded speech recognition systems, such as in mobile phones, only authorizes few KB of memory and few MIPS. In the context of HMM-based speech recognizers, each HMM-state distribution is modeled independently from to the other and has a large amount of parameters. In spite of using statetying techniques, the size of the acoustic models stays large and certain redundancy remains between states. In this paper, we investigate the capacity of the Subspace Gaussian Mixture approach to reduce the acoustic models size while keeping good performances. We introduce a simplification concerning state specific Gaussians weights estimation, which is a very complex and time consuming procedure in the original approach. With this approach, we show that the acoustic model size can be reduced by 92% with almost the same performance as the standard acoustic modeling.

Author-topic based representation of call-center conversations

Performance of Automatic Speech Recognition (ASR) systems drops dramatically when transcribing conversations recorded in noisy conditions. Speech analytics suffer from this poor automatic transcription quality. To tackle this difficulty, a solution consists in mapping transcriptions into a space of hidden topics. This abstract representation allows to substantiate the drawbacks of the ASR process. The well-known and commonly used one is the topic-based representation from a Latent Dirichlet Allocation (LDA). Several studies demonstrate the effectiveness and reliability of this high-level representation. During the LDA learning process, distribution of words into each topic is estimated automatically. Nonetheless, in the context of a classification task, no consideration is made for the targeted classes. Thus, if the targeted application is to find out the main theme related to a dialogue, this information should be taken into consideration. In this paper, we propose to compare a classical topic-based representation of a dialogue, with a new one based not only on the dialogue content itself (words), but also on the theme related to the dialogue. This original representation is based on the author-topic (AT) model. The effectiveness of the proposed representation is evaluated on a classification task from automatic dialogue transcriptions between an agent and a customer of the Paris Transportation Company. Experiments confirmed that this author-topic model approach outperforms by far the classical topic representation, with a substantial gain of more than 7% in terms of correctly labeled conversations.

Compact multiview representation of documents based on the total variability space

Mapping text documents in an LDA-based topic-space is a classical way to extract high-level representation of text documents. Unfortunately, LDA is highly sensitive to hyper-parameters related to the number of classes, or word and topic distribution, and there is no systematic way to pre-estimate optimal configurations. Moreover, various hyper-parameter configurations offer complementary views on the document. In this paper, we propose a method based on a two-step process that, first, expands the representation space by using a set of topic spaces and, second, compacts the representation space by removing poorly relevant dimensions. These two steps are based respectively on multi-view LDA-based representation spaces and factor-analysis models. This model provides a view-independent representation of documents while extracting complementary information from a massive multi-view representation. Experiments are conducted on the DECODA conversation corpus and the Reuters-21578 textual dataset. Results show the efficiency of the proposed multiview compact representation paradigm. The proposed categorization system reaches an accuracy of 86.5% with automatic transcriptions of conversations from DECODA corpus and a Macro-F1 of 80% during a classification task of the well-known Reuters-21578 corpus, with a significant gain compared to the baseline (best single topic space configuration), as well as methods and document representations previously studied.

Integration of Word and Semantic Features for Theme Identification in Telephone Conversations

The paper describes a research about the possibility of integrating different types of word and semantic features for automatically identifying themes of real-life telephone conversations in a customer care service (CCS). Features are all the words of the application vocabulary, the probabilities obtained with latent Dirichlet allocation (LDA) of selected discriminative words and semantic features obtained with a limited human supervision of words and patterns expressing entities and relations of the application ontology. A deep neural network (DNN) is proposed for integrating these features. Experimental results on manual and automatic conversation transcriptions are presented showing the effective contribution of the integration. The results show how to automatically select a large subset of the test corpus with high precision and recall, making it possible to automatically obtain theme mention proportions in different time periods.

Factor analysis based session variability compensation for Automatic Speech Recognition

In this paper we propose a new feature normalization based on Factor Analysis (FA) for the problem of acoustic variability in Automatic Speech Recognition (ASR). The FA paradigm was previously used in the field of ASR, in order to model the usefull information: the HMM state dependent acoustic information. In this paper, we propose to use the FA paradigm to model the useless information (speaker- or channel-variability) in order to remove it from acoustic data frames. The transformed training data frames are then used to train new HMM models using the standard training algorithm. The transformation is also applied to the test data before the decoding process. With this approach we obtain, on french broadcast news, an absolute WER reduction of 1.3%.

Acoustic modeling for under-resourced languages based on vectorial HMM-states representation using Subspace Gaussian Mixture Models

This paper explores a novel method for context-dependent models in automatic speech recognition (ASR), in the context of under-resourced languages. We present a simple way to realize a tying states approach, based on a new vectorial representation of the HMM states. This vectorial representation is considered as a vector of a low number of parameters obtained by the Subspace Gaussian Mixture Models paradigm (SGMM). The proposed method does not require phonetic knowledge or a large amount of data, which represent the major problems of acoustic modeling for under-resourced languages. This paper shows how this representation can be obtained and used for tying states. Our experiments, applied on Vietnamese, show that this approach achieves a stable gain compared to the classical approach which is based on decision trees. Furthermore, this method appears to be portable to other languages, as shown in the preliminary study conducted on Berber.

Subspace Gaussian Mixture Models for vectorial HMM-states representation

In this paper we present a vectorial representation of the HMM states that is inspired by the Subspace Gaussian Mixture Models paradigm (SGMM). This vectorial representation of states will make possible a large number of applications, such as HMM-states clustering and graphical visualization. Thanks to this representation, the Hidden Markov Model (HMM) states can be seen as sets of points in multi-dimensional space and then can be studied using statistical data analysis techniques. In this paper, we show how this representation can be obtained and used for tying states of an HHM-based automatic speech recognition system without any use of linguistic or phonetic knowledge. In experiments, this approach achieves significant and stable gain, while conserving the classical approach based on decision trees. We also show how it can be used for graphical visualization, which can be useful in other domains like phonetics or clinical phonetics.