Deep features, defined as the activations of hidden layers of a neural network, have given promising results applied to various vision tasks. In this paper, we explore the usefulness and transferability of deep features, applied in the context of the problem of keyword spotting (KWS). We use a state-ofthe-art deep convolutional network to extract deep features. The optimal parameters concerning...

Most existing methods for audio sentiment analysis use automatic speech recognition to convert speech to text, and feed the textual input to text-based sentiment classifiers. This study shows that such methods may not be optimal, and proposes an alternate architecture where a single keyword spotting system (KWS) is developed for sentiment detection. In the new architecture, the text-based senti...

This paper addresses the problem of detecting keywords in unconstrained speech without explicit modeling of non-keyword segments. The proposed algorithms are 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. We can also us...

An ergodic hidden Markov model (EHMM) of speech can be trained in an unsupervised manner using unlabeled speech. A keyword spotting system has been developed where the queries and test observations are represented as sequences of states of the EHMM. A graphical keyword model is built by aggregating multiple instances of a query or by using mappings between phonemes and states of the EHMM. A mod...

Comparing spoken dialogue systems in commercial and research background reveals a great discrepancy in state-of-the-art systems. Whereas research systems tend to be very complex, allowing free and exible dialogues using unrestricted speech, commercial systems are restricted to far less complex solutions such as menu-driven systems based on simple keyword-spotting, mostly doing completely withou...

In this paper, we propose a novel lower-bound estimate for dynamic time warping (DTW) methods that use an inner product distance on multi-dimensional posterior probability vectors known as posteriorgrams. Compared to our previous work, the new lower-bound estimate uses piecewise aggregate approximation (PAA) to reduce the time required for calculating the lower-bound estimate. We describe the P...

Confidence measure plays an important role in keyword spotting. To enhance the effectiveness of the confidence measure, we propose a novel method which improves the performance of keyword spotting by directly maximizing the area under the ROC curve (AUC). Firstly, we approximate the AUC as an objective function with the weighted mean confidence measure. Then, we optimize the objective function ...

Detection of speech attributes, phones and words is a key component of a detection-based automatic speech recognition framework in the automatic speech attribute transcription project. This paper presents a two-stage approach, keywordfiller network method followed by knowledge-based pruning and rescoring, for detection of any given word in continuous speech. Different from conventional keyword ...

Mainly for the sake of solving the lack of keyword-specific data, we propose one Keyword Spotting (KWS) system using Deep Neural Network (DNN) and Connectionist Temporal Classifier (CTC) on power-constrained small-footprint mobile devices, taking full advantage of general corpus from continuous speech recognition which is of great amount. DNN is to directly predict the posterior of phoneme unit...

We consider feature learning for efficient keyword spotting that can be applied in severely under-resourced settings. The objective is to support humanitarian relief programmes by the United Nations parts of Africa which almost no language resources are available. For rapid development such languages, we rely on a small, easily-compiled set isolated keywords. These templates large corpus in-dom...