Effective Combination of DenseNet and BiLSTM for Keyword Spotting

Comparison of Multiple System Combination Techniques for Keyword Spotting

System combination is a common approach to improving results for both speech transcription and keyword spotting—especially in the context of low-resourced languages where building multiple complementary models requires less computational effort. Using state-of-the-art CNN and DNN acoustic models, we analyze the performance, cost, and trade-offs of four system combination approaches: feature com...

Morphological Segmentation for Keyword Spotting

• We explore the impact of morphological segmentation on Keyword Spotting (KWS). ! • Handling out-of-vocabulary (OOV) words is a major challenge in KWS we aim to alleviate this problem by utilizing sub-word units.! • We augment a state-of-the-art KWS system with subword units derived from supervised and unsupervised morphological segmentations, and compare with phonetic and syllabic segmentatio...

Discriminative keyword spotting

This paper proposes a new approach for keyword spotting, which is not based on HMMs. The proposed method employs a new discriminative learning procedure, in which the learning phase aims at maximizing the area under the ROC curve, as this quantity is the most common measure to evaluate keyword spotters. The keyword spotter we devise is based on nonlinearly mapping the input acoustic representat...

Low Resource Keyword Spotting

Assisted keyword indexing for lecture videos using unsupervised keyword spotting

Many students use videos to supplement learning outside the classroom. This is particularly important for students with challenged visual capacities, for whom seeing the board during lecture is di cult. For these students, we believe that recording the lectures they attend and providing e↵ective video indexing and search tools will make it easier for them to learn course subject matter at their...