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3. Commands & distress calls recognition This section describes the “Commands & distress calls recognition” module of the system. The audio signal is continuously acquired from the microphone and processed by a VAD to separate speech and silence portions of the signal. The “Interference Canceller” reduces the disturbance coming from known audio sources, such as a television or a radio. The “Vocal Effort Classifier” detects the type of vocal effort of the speech signal, so that the most suitable acoustic model can be employed by the “Speech Recogniser”. This is based on the open source engine PocketSphinx (Huggins-Daines et al., 2006), and it operates without the need for a keyphrase for being activated. 3.1. Voice Activity Detection The Voice Activity Detector algorithm discriminates between speech and silence portions of the acquired audio signal. The employed algorithm here operates following an energy based approach : when the audio pipeline is firstly started, the initial frames of the signal are employed to estimate the background noise statistics. Then, a frame of the signal is classified as “speech” if its energy exceeds the one of the background noise. 3.2. Interference Cancellation The block diagram of the interference cancellation algorithm is shown in 365 Fig. 6. Notice that the audio signal is acquired by means of a microphone array, thus an interference canceller for each audio channel is present. The term x(n) represents the interference reference signal acquired from the output of the source, w(n) ∗ x(n) is a filtered version of x(n) with the room impulse response w(n) and s(n) is the speech of the local speaker, which added to w(n) ∗ x(n) gives d(n) = s(n) + w(n) ∗ x(n). The purpose of the interference cancellation algorithm is to obtain an estimate of w(n) ∗ x(n), ˆy(n), by identifying the room impulse response w(n) so that the output e(n) contains only a non-distorted version of s(n). The problem is the same of acoustic echo cancellation in hands-free commu nication, where x(n) is the far-end signal coming from a remote speaker. The algorithm for interference cancellation is thus the one implemented in the Speex codec. In particular, the algorithm is based on the Multidelay Block Frequency Domain (MDF) adaptive filter, and on a dynamically varied adaptation stepsize that handles double-talk conditions automatically. For additional details, 380 please refer to Valin (2007). 3.3. Vocal effort classification A speech signal can be uttered in five different vocal efforts (Zhang & Hansen, 2007): whispered, soft, neutral, loud, and shouted. Speech recogniser acoustic models are usually trained with neutral speech corpora, and the mismatch that 385 can occur in the recognition phase due to the different vocal effort of the input utterance can considerably degrade the performance . The vocal effort classification algorithm detects the type of vocal effort of the input utterance. The information is then employed for selecting the acoustic model that reduces the impact of vocal effort variability. More details on this will be provided in Section 3.4.2. The algorithm employed here has been presented and assessed in Principi et al. (2014a), and it will be now briefly reminded. The block diagram of the approach is shown in Fig. 7. The method is similar to the GMM-UBM/SVM paradigm of speaker recognition systems  with Universal Background Model (UBM) representing 395 the entire acoustic space and modelled by means of a Gaussian Mixture Model (GMM). Training of the UBM is performed using the expectation maximization algorithm (Bilmes, 1998) on a large corpus U of neutral speech utterances. Considering then a set of N vocal efforts corpora Vi i = 1, 2, . . . , N, for each utterance of each vocal effort corpus a GMM supervector is obtained in 400 the “Supervectors extraction” block by adapting the UBM with Maximum APosteriori (MAP) algorithm and concatenating the mean values. The final step of the training phase is the estimation of the SVM parameters. In this work, an SVM with a radial basis function has been used. Classification is performed extracting the supervector from an input utter ance as in the training phase, and then determining the vocal effort type evaluating the SVM discriminant function. Since the number of vocal effort can be greater than two, and SVMs are binary classifiers, the “one versus all” (Bishop, 2006) technique has been adopted. LIBSVM (Chang & Lin, 2011) has been employed both in the training and testing phases of the SVM.