In spite of its numerous potential applications, Automatic Speech Recognition (ASR) remains a difficult (and mainly unsolved) problem. In addition to the intrinsic difficulty of the task, users tend to go beyond the pre-defined lexicon words, and the important keywords necessary to understand voice requests are often lost in extra words. In this context, it is often interesting to develop Keywo...

We present the three approaches submitted to the Spoken Web Search. Two of them rely on Acoustic Keyword Spotting (AKWS) while the other relies on Dynamic Time Warping. Features are 3-state phone posterior. Results suggest that applying a Karhunen-Loeve transform to the log-phone posteriors representing the query to build a GMM/HMM for each query and a subsequent AKWS system performs the best.

We explore using Convolutional Neural Networks (CNNs) for a small-footprint keyword spotting (KWS) task. CNNs are attractive for KWS since they have been shown to outperform DNNs with far fewer parameters. We consider two different applications in our work, one where we limit the number of multiplications of the KWS system, and another where we limit the number of parameters. We present new CNN...

Keyword spotting (KWS) constitutes a major component of human-technology interfaces. Maximizing the detection accuracy at a low false alarm (FA) rate, while minimizing the footprint size, latency and complexity are the goals for KWS. Towards achieving them, we study Convolutional Recurrent Neural Networks (CRNNs). Inspired by large-scale state-ofthe-art speech recognition systems, we combine th...

A project report submitted during summer internship under the supervision of Prof. Abstract Multi‐lingual interfaces can be of great use in a number of applications. A very important issue for such systems is to first identify the segments of utterances corresponding to a specific language. Language boundary information is also very vital before any further processing can be done. Language spec...

We investigate the use of sub-word lexical units for the detection of out-of-vocabulary (OOV) keywords in the keyword spotting task. Sub-word units based on morphological decomposition and character ngrams are compared. In particular, we examine the benefit of sub-word units that cross word boundaries. Experiments are performed on the IARPA Babel Turkish dataset. Our results demonstrate that cr...

This paper addresses the problem of detecting keywords in unconstrained speech without explicit modeling of nonkeyword segments. The proposed algorithm is based on recent developments in confidence measures using local posterior probabilities, and searches for the segment maximizing the average observation posterior’ along the most likely path in the hypothesized keyword model.’ As known, this ...

This paper presents a framework of using Chinese speech to access images via English captions. The formulation and the structure mapping rules of Chinese and English named entities are extracted from an NICT foreign location name corpus. For a named location, name part and keyword part are usually transliterated and translated, respectively. Keyword spotting identifies the keyword from speech q...

This paper presents a study of the influence of acoustic variability on topic spotting performance in an application involving automatic indexing of course lectures. The application involves users formulating keyword queries to an indexing system which includes phone lattice based acoustic representations of audio material, a mechanism for keyword searching of a phone lattice, and a measure for...

In this paper, we propose a new posterior based scoring approach for keyword and non keyword (garbage) elements. The estimation of these scores is based on HMM state posterior probability definition, taking into account long contextual information and the prior knowledge (e.g. keyword model topology). The state posteriors are then integrated into keyword and garbage posteriors for every frame. ...