For reliable speech recognition, it is necessary to handle the usage environments. In this study, we target voice-driven multi-unmanned aerial vehicles (UAVs) control. Although many studies have introduced several systems for voice-driven UAV control, most have focused on a general speech recognition architecture to control a single UAV. However, for stable voice-controlled driving, it is essential to handle the environmental conditions of UAVs carefully, including environmental noise that deteriorates recognition accuracy, and the operating scheme, e.g., how to direct a target vehicle among multiple UAVs and switch targets using speech commands. To handle these issues, we propose an efficient, vehicle-embedded speech recognition front-end for multi-UAV control via voice. First, we propose a noise reduction approach that considers non-stationary noise in outdoor environments. The proposed method improves the conventional minimum mean squared error (MMSE) approach to handle non-stationary noises, e.g., babble and vehicle noises. In addition, we propose a multi-channel voice trigger method that can control multiple UAVs while efficiently directing and switching the target vehicle via speech commands. We evaluated the proposed methods on speech corpora, and the experimental results demonstrate that the proposed methods outperform the conventional approaches. In trigger word detection experiments, our approach yielded approximately 7%, 12%, and 3% relative improvements over spectral subtraction, adaptive comb filtering, and the conventional MMSE, respectively. In addition, the proposed multi-channel voice trigger approach achieved approximately 51% relative improvement over the conventional approach based on a single trigger word.

The performance of automatic speech recognition (ASR) may be degraded when accented speech is recognized because the speech has some linguistic differences from standard speech. Conventional accented speech recognition studies have utilized the accent embedding method, in which the accent embedding features are directly fed into the ASR network. Although the method improves the performance of accented speech recognition, it has some restrictions, such as increasing the computational costs. This study proposes an efficient method of training the ASR model for accented speech in a domain adversarial way based on the Domain Adversarial Neural Network (DANN). The DANN plays a role as a domain adaptation in which the training data and test data have different distributions. Thus, our approach is expected to construct a reliable ASR model for accented speech by reducing the distribution differences between accented speech and standard speech. DANN has three sub-networks: the feature extractor, the domain classifier, and the label predictor. To adjust the DANN for accented speech recognition, we constructed these three sub-networks independently, considering the characteristics of accented speech. In particular, we used an end-to-end framework based on Connectionist Temporal Classification (CTC) to develop the label predictor, a very important module that directly affects ASR results. To verify the efficiency of the proposed approach, we conducted several experiments of accented speech recognition for four English accents including Australian, Canadian, British (England), and Indian accents. The experimental results showed that the proposed DANN-based model outperformed the baseline model for all accents, indicating that the end-to-end domain adversarial training effectively reduced the distribution differences between accented speech and standard speech.