# Voice Activation for Low-Resource Languages
###### Abstract  
Voice activation systems are used to find a pre-defined word or phrase in the audio stream. Industry solutions, such as \"OK, Google\" for Android devices, are trained with millions of samples. In this work, we propose and investigate several ways to train a voice activation system when the in-domain data set is small. We compare self-training exemplar pre-training, fine-tuning a model pre-trained on another domain, joint training on both an out-of-domain high-resource and a target low-resource data set, and unsupervised pre-training. In our experiments, the unsupervised pre-training and the joint-training with a high-resource data set from another domain significantly outperform a strong baseline of fine-tuning a model trained on another data set. We obtain 7-25% relative improvement depending on the model architecture. Additionally, we improve the best test accuracy on the Lithuanian data set from 90.77% to 93.85%.  
Acoustic model.
* Decision-making model.  
In the first part, the acoustic features are extracted from the source audio. Typically, it is done by segmenting the audio in short, possibly overlapping, frames, then computing the features in each frame. Many research studies have used log-mel filterbanks [7; 8; 9; 10; 11].  
The acoustic model is used to compute the probability of some acoustic event, given the observed audio. There are several popular choices for acoustic events: phonemes [6; 12; 13] and words [14; 15] are widely used. Usually, this module is the most resource demanding in the voice activation system.  
Finally, the decision-making module is used to choose between several options: whether there is a keyword in the processed audio segment and if so, which one it is. This module can be quite complicated, especially in the case of phoneme-based spotters [16]. In the case of using the whole keywords as acoustic events, the decision-making module is usually just comparing the resulting probabilities with a threshold.  
Historically, voice activation systems were based on hidden Markov models [17] or pattern matching approaches, such as dynamic time wrapping [18]. However, since 1990, top-performing voice activation systems have been using (almost exclusively) neural networks as an acoustic model [16; 19; 20; 21; 22].  
Neural networks are powerful approximators and widely used in many areas. One of the drawbacks of using neural networks is that in many cases, a large labeled data set is needed to train such a model. For example, the authors of [16] use hundreds of thousands of samples per keyword and the authors or [23] use millions.  
However, in some cases, it is not practical or even hardly possible to collect a large data set. In the case of designing a voice activation system for custom, user-defined keywords, it might hurt the user experience if it is necessary to repeat the custom keyword  
Figure 1: The scheme of a typical voice activation system.  
many times before real usage. Another example is quickly adapting the system to a new sensitive keyword in the aforementioned crime analysis application. Creating a voice activation system for low-resource languages, such as Lithuanian or Latvian, could be another example when the effective use of a small data set is needed, because it is difficult to find enough native speakers for creating a large audio database.  
In this work, we investigate several known and new ways to improve the quality of a voice activation system on a low-resource labeled data set, comparing to the standard supervised way of acoustic model training. We use a small Lithuanian data set [24] in order to compare the investigated methods. In all our experiments, we use the same neural network architecture but introduce some changes to audio features (Section 3.4.1), use different kinds of pre-training (Sections 3.4.2 and 3.4.3) or modify the training procedure (Sections 3.4.4 and 3.4.5).