## 3 Vehicle-Embedded Speech Recognition Front-End for Multi-UAV Control
### Voice Trigger for Multiple UAVs Control
#### 3.3.2 Multi-Channel Voice Trigger for Multi-UAV Control
Here, the trigger recognition module processes two or more trigger word models with the same number as UAVs, whereas the conventional approach only processes a single trigger model (Figure 6). In addition to the trigger word models, we also employ a filtering model that can withdraw non-trigger speech signals. For acoustic features, each model (i.e., the trigger word models and filtering model) produces a recognition result that represents the similarity between the input speech and each model. Then, the maximum value of the results calculated by the trigger models is compared to the result of the filtering model. If the maximum value is greater than the result of the filtering model, the speech region is determined as the trigger word corresponding to the model giving the maximum value. The other case means that the region demonstrates greater similarity with the filtering model than the trigger word models. Thus, the region is considered to be a non-trigger voice and discarded. The use of multiple trigger word models enables us to handle two or more trigger words, thereby realizing multi-UAV control. Thus, it can be characterized by a multi-channel voice trigger. The filtering model determines trigger words by filtering out non-trigger voices. This approach allows us to determine trigger words without using a fixed threshold, while the conventional approach is highly dependent on such a threshold. The abovementioned models are acoustic models used in general speech recognition tasks, and they are constructed prior to the recognition stage, using machine learning techniques, e.g., stochastic modeling or a neural network. Although the DNN-based approach provides stable speech recognition performance, it has high computational complexity. Thus, most conventional DNN approaches have focused on continuous speech recognition of large vocabularies. Our target task is trigger word  
Figure 7: Procedures of proposed multi-channel voice trigger-based front-end and speech recognition. recognition with a limited number of trigger words. Thus, a stochastic modeling approach, e.g., HMM, that is capable of rapid recognition with low complexity is optimal. The final filtering process is performed by the post-processing module, which attempts to verify the correctness of the detected trigger word. Thus, we designate the trigger voice region determined in the previous recognition process as a candidate trigger region. A main role of the final process is to determine if the candidate region is finally accepted as a trigger word or rejected as a recognition error. For the decision criterion, we employ the likelihood results produced as an HMM-based speech recognition result. The likelihood results refer to the observation probability, which indicates how closely the speech signals are observed in a given model. After obtaining the observation probability for each model, the results are ranked according to the values. The recognition result is determined as a model with the highest probability, i.e., the top rank in the likelihood results. Figure 8 illustrates the overall procedures of the proposed post-processing for trigger word verification. The verification is performed using a decision criterion based on the likelihood results. The following equations describe the proposed decision criteria. \[DC_{1}(x)=\frac{P(x\big{|}R_{1}(x))}{\sum\limits_{r=1}^{N}P(x\big{|}R_{r}(x))}\,, \tag{10}\]  
\[DC_{2}(x)=P(x|R_{1}(x))-\frac{1}{N}\!\sum\limits_{r=1}^{N}P(x\big{|}R_{r}(x))\,, \tag{11}\]  
where \(R_{r}(x)\) denotes a model at the \(r\)-th rank in the likelihood results estimated from the trigger models and a filtering model for candidate trigger region \(x\). \(P(x\big{|}R_{r}(x))\) denotes the observation probability estimated for \(R_{r}(x)\). The two criterion functions calculate the difference between the observation probability in the top-ranked model (\(R_{1}(x)\)) and those in other models. This idea considers a general property of observation probability obtained in recognition procedure.