In specialized domains like Air Traffic Control (ATC), a notable challenge in porting a deployed Automatic Speech Recognition (ASR) system from one airport to another is the alteration in the set of crucial words that must be accurately detected in the new environment. Typically, such words have limited occurrences in training data, making it impractical to retrain the ASR system. This paper explores innovative word-boosting techniques to improve the detection rate of such rare words in the ASR hypotheses for the ATC domain. Two acoustic models are investigated: a hybrid CNN-TDNNF model trained from scratch and a pre-trained wav2vec2-based XLSR model fine-tuned on a common ATC dataset. The word boosting is done in three ways. First, an out-of-vocabulary word addition method is explored. Second, G-boosting is explored, which amends the language model before building the decoding graph. Third, the boosting is performed on the fly during decoding using lattice re-scoring. The results indicate that the G-boosting method performs best and provides an approximately 30-43% relative improvement in recall of the boosted words. Moreover, a relative improvement of up to 48% is obtained upon combining G-boosting and lattice-rescoring.

Movie genre prediction from trailers is mostly attempted in a multi-modal manner. However, the characteristics of movie trailer audio indicate that this modality alone might be highly effective in genre prediction. Movie trailer audio predominantly consists of speech and music signals in isolation or overlapping conditions. This work hypothesizes that the genre labels of movie trailers might relate to the composition of their audio component. In this regard, speech-music confidence sequences for the trailer audio are used as a feature. In addition, two other features previously proposed for discriminating speech-music are also adopted in the current task. This work proposes a time and channel Attention Convolutional Neural Network (ACNN) classifier for the genre classification task. The convolutional layers in ACNN learn the spatial relationships in the input features. The time and channel attention layers learn to focus on crucial timesteps and CNN kernel outputs, respectively. The Moviescope dataset is used to perform the experiments, and two audio-based baseline methods are employed to benchmark this work. The proposed feature set with the ACNN classifier improves the genre classification performance over the baselines. Moreover, decent generalization performance is obtained for genre prediction of movies with different cultural influences (EmoGDB).

Detection of speech and music signals in isolated and overlapped conditions is an essential preprocessing step for many audio applications. Speech signals have wavy and continuous harmonics, while music signals exhibit horizontally linear and discontinuous harmonic patterns. Music signals also contain more percussive components than speech signals, manifested as vertical striations in the spectrograms. In case of speech music overlap, it might be challenging for automatic feature learning systems to extract class-specific horizontal and vertical striations from the combined spectrogram representation. A pre-processing step of separating the harmonic and percussive components before training might aid the classifier. Thus, this work proposes the use of harmonic-percussive source separation method to generate features for better detection of speech and music signals. Additionally, this work also explores the traditional and cascaded-information multi-task learning (MTL) frameworks to design better classifiers. MTL framework aids the training of the main task by employing simultaneous learning of several related auxiliary tasks. Results have been reported both on synthetically generated speech music overlapped signals and real recordings. Four state-of-the-art approaches are used for performance comparison. Experiments show that harmonic and percussive decomposition of spectrograms perform better as features. Moreover, the MTL-framework based classifiers further improve performances.

Spectrograms of speech and music contain distinct striation patterns. Traditional features represent various properties of the audio signal but do not necessarily capture such patterns. This work proposes to model such spectrogram patterns using a novel Spectral Peak Tracking (SPT) approach. Two novel time-frequency features for speech vs. music classification are proposed. The proposed features are extracted in two stages. First, SPT is performed to track a preset number of highest amplitude spectral peaks in an audio interval. In the second stage, the location and amplitudes of these peak traces are used to compute the proposed feature sets. The first feature involves the computation of mean and standard deviation of peak traces. The second feature is obtained as averaged component posterior probability vectors of Gaussian mixture models learned on the peak traces. Speech vs. music classification is performed by training various binary classifiers on these proposed features. Three standard datasets are used to evaluate the efficiency of the proposed features for speech/music classification. The proposed features are benchmarked against five baseline approaches. Finally, the best-proposed feature is combined with two contemporary deep-learning based features to show that such combinations can lead to more robust speech vs. music classification systems.