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Deep learning is at the core of recent spoken language understanding (SLU) related tasks. More precisely, deep neural networks (DNNs) drastically increased the performances of SLU systems, and numerous architectures have been proposed. In the real-life context of theme identification of telephone conversations, it is common to hold both a human, manual (TRS) and an automatically transcribed (ASR) versions of the conversations. Nonetheless, and due to production constraints, only the ASR transcripts are considered to build automatic classifiers. TRS transcripts are only used to measure the performances of ASR systems. Moreover, the recent performances in term of classification accuracy, obtained by DNN related systems are close to the performances reached by humans, and it becomes difficult to further increase the performances by only considering the ASR transcripts. This paper proposes to distillates the TRS knowledge available during the training phase within the ASR representation, by using a new generative adversarial network called M2H-GAN to generate a TRS-like version of an ASR document, to improve the theme identification performances., Comment: Submitted at INTERSPEECH 2019

International audience; In the last decades, encoder-decoders or autoencoders (AE) have received a great interest from researchers due to their capability to construct robust representations of documents in a low dimensional subspace. Nonetheless, autoencoders reveal little in way of spoken document internal structure by only considering words or topics contained in the document as an isolate basic element, and tend to overfit with small corpus of documents. Therefore, Quaternion Multi-layer Perceptrons (QMLP) have been introduced to capture such internal latent dependencies , whereas denoising autoencoders (DAE) are composed with different stochastic noises to better process small set of documents. This paper presents a novel autoencoder based on both hitherto-proposed DAE (to manage small corpus) and the QMLP (to consider internal latent structures) called "Quater-nion denoising encoder-decoder" (QDAE). Moreover, the paper defines an original angular Gaussian noise adapted to the speci-ficity of hyper-complex algebra. The experiments, conduced on a theme identification task of spoken dialogues from the DE-CODA framework, show that the QDAE obtains the promising gains of 3% and 1.5% compared to the standard real valued de-noising autoencoder and the QMLP respectively.

International audience; Many Proper Names (PNs) are Out-Of-Vocabulary (OOV) words for speech recognition systems used to process di-achronic audio data. To enable recovery of the PNs missed by the system, relevant OOV PNs can be retrieved by exploiting the semantic context of the spoken content. In this paper, we explore the Neural Bag-of-Words (NBOW) model, proposed previously for text classification, to retrieve relevant OOV PNs. We propose a Neural Bag-of-Weighted-Words (NBOW2) model in which the input embedding layer is augmented with a context anchor layer. This layer learns to assign importance to input words and has the ability to capture (task specific) keywords in a NBOW model. With experiments on French broadcast news videos we show that the NBOW and NBOW2 models outper-form earlier methods based on raw embeddings from LDA and Skip-gram. Combining NBOW with NBOW2 gives faster convergence during training.

International audience; This paper describes a research on topic identification in a real-world customer service telephone conversations between an agent and a customer. Separate hidden spaces are considered for agents, customers and the combination of them. The purpose is to separate semantic constituents from the speaker types and their possible relations. Probabilities of hidden topic features are then used by separate Gaussian classifiers to compute theme probabilities for each speaker type. A simple strategy, that does not require any additional parameter estimation, is introduced to classify themes with confidence indicators for each theme hypothesis. Experimental results on a real-life application show that the use of features from speaker type specific hidden spaces capture useful semantic contents with significantly superior performance with respect to independent word-based features or a single set of features. Experimental results also show that the proposed strategy makes it possible to perform surveys on collections of conversations by automatically selecting processed samples with high theme identification accuracy.

International audience; Retrieving pronounced person names in spoken documents is a critical problematic in the context of audiovisual content indexing. In this paper, we present a cascading strategy for two methods dedicated to spoken name recognition in speech. The first method is an acoustic name spotting in phoneme confusion networks. It is based on a phonetic edition distance criterion based on phoneme probabilities held in confusion networks. The second method is a continuous context modelling approach applied on the 1-best transcription output. It relies on a probabilistic modelling of name-to-context dependencies. We assume that the combination of these methods, based on different types of information, may improve spoken name recognition performance. This assumption is studied through experiments done on a set of audiovisual documents from the development set of the REPERE challenge. Results report that combining acoustic and linguistic methods produces an absolute gain of 3% in terms of F-measure compared to the best system taken alone.

In this paper, a new strategy for a fast adaptation of acoustic models is proposed for embedded speech recognition. It relies on a general GMM, which represents the whole acoustic space, associated with a set of HMM state-dependent probability functions modeled as transformations of this GMM. The work presented here takes advantage of this architecture to propose a fast and efficient way to adapt the acoustic models. The adaptation is performed only on the general GMM model, using techniques gathered from the speaker recognition domain. It does not require state-dependent adaptation data and it is very efficient in terms of computational cost. Weevaluate our approach in the voice-command task, using a car-based corpus. This adaptation method achieved a relative error-rate decrease of about 10% even if few adaptation data are available. The complete system allows a total relative gain of more than 20% compared to a basic HMM-based system. Index Terms: speech recognition, compact acoustic models, adaptation

In this paper a new computation and approximation scheme for Language Model Look-Ahead (LMLA) is introduced. The main benefit of LMLA is sharper pruning of the search space during the LVCSR decoding process. However LMLA comes with its own cost and is known to scale badly with both LM n-gram order and LM size. The proposed method tackles this problem with a divide and conquer approach which enables faster computation without additional WER cost. The obtained results allowed our system to participate in the real-time task of the ESTER Broadcast News transcription evaluation campaign for French.