In order to organize efficiently lots of hours of audio contents such as meetings, radio news, search for spoken keywords is essential. An approach uses filler models to account for non-keyword intervals. Another approach uses a large vocabulary continuous speech recognition system (LVCSR) which retrieves a word string and then search for the keywords in this string. This approach yields high p...

Transformer can effectively model long rang dependency, but suffer from uncapable to extract local feature patterns. While CNNs exploit features effectively. In this paper, we seek combine convolution and Transformers improves over using them individually, propose improved convolution-augmented transformers for keyword spotting. The are constructed with a ResNet front-end back-end in series. Us...

In dieser Zusammenfassung stellen wir einen Demonstrator zur Schlüssel-worterkennung (Keyword-Spotting) vor, der ohne vorheriges Training der zu detektie

We present a simple, yet highly accurate, spam filtering program, called SpamCop, which is able to identify about 92% of the spams while misclassifying only about 1.16% of the nonspam e-mails. SpamCop treats an e-mail message as a multiset of words and employs a na’fve Bayes algorithm to determine whether or not a message is likely to be a spam. Compared with keyword-spotting rules, the probabi...

We present a simple, yet highly accurate, spam ltering program, called Spam-Cop, which is able to identify about 92% of the spams while misclassifying only about 1.16% of the nonspam e-mails. SpamCop treats an e-mail message as a multiset of words and employs a naive Bayes algorithm to determine whether or not a message is likely to be a spam. Compared with keyword-spotting rules, the probabili...

Nearly all previous work on small-footprint keyword spotting with neural networks quantify model footprint in terms of the number of parameters and multiply operations for an inference pass. These values are, however, proxy measures since empirical performance in actual deployments is determined by many factors. In this paper, we study the power consumption of a family of convolutional neural n...

This paper addressed the problem of Out-Of-Vocabulary (OOV) utterance detection in small vocabulary telephone keyword spotting system. We propose a new approach for modeling OOV words in the scenario of a small vocabulary of telephone keyword spotting system. The paper adopt the semi-continuous Hidden Markov Model with multiple codebooks to modeling the keywords. We propose a two pass procedure...

We explore the application of deep residual learning and dilated convolutions to the keyword spotting task, using the recently-released Google Speech Commands Dataset as our benchmark. Our best residual network (ResNet) implementation significantly outperforms Google’s previous convolutional neural networks in terms of accuracy. By varying model depth and width, we can achieve compact models th...