## 4 Experiments and Discussion
### Validation of Proposed Voice Trigger Approach
#### 4.2.2 Results and Discussion  
The performance of the standard speech recognition system is evaluated relative to recognition accuracy. The evaluation of trigger word recognition differs from speech recognition because this task does not require recognizing all input speech. The trigger recognition system has two goals, i.e.,  
Figure 12: Trigger word detection performance with noisy speech. correctly detecting and recognizing trigger words, and correctly disregarding non-trigger utterances. Thus, general trigger recognition tasks use two errors for performance evaluation. The first error type occurs when the system detects a non-trigger utterance as a trigger word, and the second error type occurs when the system disregards a trigger word region as a non-trigger utterance. Such errors are referred to as false alarm and false rejection errors, respectively. In consideration of the above error types, trigger word detection performance is evaluated using the detection error tradeoff (DET) curve. The DET curve is used to observe the tendency of the two error types. Generally, the false rejection error rate is inversely proportional to the false alarm error rate because these two errors occur in an opposite manner. We evaluated the performance of three comparative approaches, i.e., the conventional approach, the proposed approach based on the first decision criterion (Proposed_DC1) described in (10), and the proposed approach based on the second decision criterion (Proposed_DC2) described in (11). The conventional voice trigger approach recognizes only a trigger word, depending on the trigger model, and uses a threshold to determine whether the recognition result is accepted or rejected (Figure 6). We consider this provides more advantages to the conventional task than handling five trigger word models simultaneously because a single model provides better conditions relative to detection performance compared to multiple models. In this experiment, we prepared five programs, each of which handled one of five trigger words. Thus, the programs were switched five times according to the trigger words such that a given program having a trigger word model could detect trigger words that are relevant to the given model. This was expected to provide better detection performance compared to when all five models participated simultaneously. That can be considered an upper bound in detection performance of the conventional approach. Note that the average of five different detection results was determined as the performance of the conventional approach. The proposed approaches attempt to recognize multiple trigger words with multiple models and a filtering model (Figure 7). In addition, the proposed approaches perform three consecutive filtering processes, including the verification process for all candidate trigger regions in post-processing (Figure 8). The programs for the proposed approaches are maintained to operate five trigger word models simultaneously for all experiments with the aim of detection and identification of trigger words. Figure 13 shows the DET curve for three approaches. A curve approaching the origin in the coordinates demonstrates better performance, thereby representing lower error rates. As can be seen, the proposed approaches significantly outperformed the conventional approach relative to both false alarm and false rejection errors. The two decision criteria were observed to function differently in trigger word detection. The second criterion provided better performance compared to the first, which represents a direct difference between the observation probability at the first rank in the likelihood results, and the other probabilities provide a better decision criterion than the ratio between them. Figure 13: Trigger word detection performance in terms of DET curve. Next, we investigated EER as another evaluation method. The EER represents an error rate in which the two types of errors indicate the same value.