# Voice Activation for Low-Resource Languages
## 2 Related Works  
This work concerns the problem of training a highly accurate voice activation system in a low-resource data set setup. This problem is tackled in several related works, most often connected to work with low-resourced languages. One of the most productive ideas is to use a bigger out-of-domain data set in one way or another. For instance, the authors of [25] use an English corpus to improve the quality of keyword spotter for the under-resourced Luganda corpus. The authors use a neural network trained as an autoencoder in two scenarios:  
* The neural network accepts audio features as input and is trained to output the same audio features. * The neural network accepts audio features as input and is trained to output the audio features of another audio file, but with same keyword as that in the input file. In both scenarios, the intermediate layers of neural network have fewer neurons than the number of audio features. This helps to build bottleneck features (the activation of neurons of intermediate levels), which hopefully contain useful information about the audio signal and less noise than the original audio features. In the second scenario, the additional property is achieved: the information about non-keyword traits (such as the gender of the speaker, the speed of pronunciation, and so on) is not necessary for the optimal model (because the model predicts the audio features of another pronunciation of the same keyword). The research studies show, experimentally, that the resulting features are better than popular mel-frequency cepstral coefficients. The neural networks are trained on relatively small data sets (still at least 10 times bigger than the setup in this work). The extracted features are used for the voice activation system, which is trained on the data sets with a size comparable to the size of the data set used in this work. The use of multilingual data is related to the methods of this research, but our setup is a bit simpler (we train the whole model on multiple data sets, not only the feature extractor, which means that the training procedure is the same for both pre-training and training, and only the data sets are different). Additionally, for some of our proposed methods, there is no need for a relatively big annotated data set in the target domain. The authors of [26] present another example of the use of multilingual data for extracting the bottleneck features. The researchers train tandem acoustic modeling for phone recognition of several languages and use bottleneck features of this model as acoustic features for speech recognition as well as keyword spotting for a low-resource target language. This idea is extended in [27] for the pre-training of hybrid speech recognition systems. The authors of [28] propose to create phone mapping between languages (namely, English and Spanish in their experiments) in order to transfer a model pre-trained on a big data set for use on a smaller data set in another language. Comparing to these methods, the methods that are investigated in our work do not require phone transcription for a corpus used for pre-training and do not require the existence of phone mapping between languages. An entirely different way to improve the quality of a keyword spotter in a low-resource setup is to generate synthetic data and use it in training. The authors of [29] use a model pre-trained on 200 million 2-second audio clips in order to extract bottleneck features. This extractor is used to train a result keyword spotter on a small data set augmented with samples generated with speech synthesis. The authors show that synthetic data are enough to build a high-quality keyword spotter, if the pre-trained feature extractor is good enough. Unfortunately good speech synthesis might not be available for some languages. Another method is to pre-train the model in an unsupervised manner. In such a way, the pre-training data set can be unlabeled, which hopefully makes it easier to obtain.