Speakers change vocal intensity in daily life to communicate over long distances and to express vocal emotions. Humans produce speech using different intensity categories (e.g. soft, normal and loud voice) and they can regulate intensity across a wide sound pressure level (SPL) range. Knowing the intensity category or the SPL of speech is beneficial in speech-based biomarking of health. Recent studies have explored the vocal intensity category classification and prediction of SPL from speech, which has been recorded without SPL calibration information and is presented on an arbitrary amplitude scale. Using speech signals in such scenario, this study investigates the wavelet scattering network (WSN) features in two tasks: (1) classification of speech into four intensity categories (soft, normal, loud, very loud) (multi-class classification task) and (2) prediction of SPL (regression task). In the former task, the WSN features showed absolute accuracy improvements of 4-14% compared to reference features. For the latter task, the WSN features improved the prediction of SPL by an average of 1-2 dB compared to the reference features.

Regulation of vocal intensity is a fundamental phenomenon in speech communication. Speakers use different intensity categories (e.g., soft, normal, and loud voice) to generate different vocal emotions or to communicate in noisy conditions or over varying distances. Vocal intensity categories have been studied in fundamental research of speech, but much less is known about their automatic classification. This study investigates the classification of vocal intensity categories from speech signals in a scenario, where the original level information of speech is absent and the signal is presented on a normalized amplitude scale. Different acoustic features were studied together with machine learning (ML) and deep learning (DL) classifiers using two different labeling approaches. Speech signals recorded from 50 speakers reciting sentences in four intensity categories (soft, normal, loud, and very loud) were analyzed. Altogether 15 feature sets including different cepstral, spectral and handcrafted (eGeMAPS) features were compared. Three ML classifiers (support vector machine, random forest and AdaBoost), and four DL classifiers (deep neural network, convolutional neural network, recurrent neural network and bidirectional long short-term memory network) were compared. The best classification accuracy of 86.0% was obtained by combining the best performing cepstral and spectral features and using the bidirectional long short-term memory classifier. • Multi-class classification of vocal intensity categories is studied. • The classification is studied using amplitude-normalized speech signals. • Various labelling approaches, features and classifiers are compared. • DL models outperformed ML models, with BiLSTM achieving the best performance.

Speakers regulate vocal intensity on many occasions for example to be heard over a long distance or to express vocal emotions. Humans can regulate vocal intensity over a wide sound pressure level (SPL) range and therefore speech can be categorized into different vocal intensity categories. Recent machine learning experiments have studied classification of vocal intensity category from speech signals which have been recorded without SPL information and which are represented on arbitrary amplitude scales. By fine-tuning four pre-trained models (wav2vec2-BASE, wav2vec2-LARGE, HuBERT, audio speech transformers), this paper studies classification of speech into four intensity categories (soft, normal, loud, very loud), when speech is presented on such arbitrary amplitude scale. The fine-tuned model embeddings showed absolute improvements of 5% and 10-12% in accuracy compared to baselines for the target intensity category label and the SPL-based intensity category label, respectively.

Automatic speech-based severity level classification of Parkinson’s disease (PD) enables objective assessment and earlier diagnosis. While many studies have been conducted on the binary classification task to distinguish speakers in PD from healthy controls (HCs), clearly fewer studies have addressed multi-class PD severity level classification problems. Furthermore, in studying the three main issues of speech-based classification systems—speaking tasks, features, and classifiers—previous investigations on the severity level classification have yielded inconclusive results due to the use of only a few, and sometimes just one, type of speaking task, feature, or classifier in each study. Hence, a systematic comparison is conducted in this study between different speaking tasks, features, and classifiers. Five speaking tasks (vowel task, sentence task, diadochokinetic (DDK) task, read text task, and monologue task), four features (phonation, articulation, prosody, and their fusion), and four classifier architectures (support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and AdaBoost) were compared. The classification task studied was a 3-class problem to classify PD severity level as healthy vs. mild vs. severe. Two MDS-UPDRS scales (MDS-UPDRS-III and MDS-UPDRS-S) were used for the ground truth severity level labels. The results showed that the use of the monologue task and the articulation and fusion of features improved classification accuracy significantly compared to the use of the other speaking tasks and features. The best classification systems resulted in a rate of accuracy of 58% (using the monologue task with the articulation features) for the MDS-UPDR-III scale and 56% (using the monologue task with fusion of features) for the MDS-UPDRS-S scale.

Vocal intensity, which is quantified typically with the sound pressure level (SPL), is a key feature of speech. To measure SPL from speech recordings, a standard calibration tone (with a reference SPL of 94 dB or 114 dB) needs to be recorded together with speech. However, most of the popular databases that are used in areas such as speech and speaker recognition have been recorded without calibration information by expressing speech on arbitrary amplitude scales. Therefore, information about vocal intensity of the recorded speech, including SPL, is lost. In the current study, we introduce a new open and calibrated speech/electroglottography (EGG) database named Aalto Vocal Intensity Database (AVID). AVID includes speech and EGG produced by 50 speakers (25 males, 25 females) who varied their vocal intensity in four categories (soft, normal, loud and very loud). Recordings were conducted using a constant mouth-to-microphone distance and by recording a calibration tone. The speech data was labelled sentence-wise using a total of 19 labels that support the utilisation of the data in machine learing (ML) -based studies of vocal intensity based on supervised learning. In order to demonstrate how the AVID data can be used to study vocal intensity, we investigated one multi-class classification task (classification of speech into soft, normal, loud and very loud intensity classes) and one regression task (prediction of SPL of speech). In both tasks, we deliberately warped the level of the input speech by normalising the signal to have its maximum amplitude equal to 1.0, that is, we simulated a scenario that is prevalent in current speech databases. The results show that using the spectrogram feature with the support vector machine classifier gave an accuracy of 82% in the multi-class classification of the vocal intensity category. In the prediction of SPL, using the spectrogram feature with the support vector regressor gave an mean absolute error of about 2 dB and a coefficient of determination of 92%. We welcome researchers interested in classification and regression problems to utilise AVID in the study of vocal intensity, and we hope that the current results could serve as baselines for future ML studies on the topic.

Automatic detection and severity level classification of dysarthria directly from acoustic speech signals can be used as a tool in medical diagnosis. In this work, the pre-trained wav2vec 2.0 model is studied as a feature extractor to build detection and severity level classification systems for dysarthric speech. The experiments were carried out with the popularly used UA-speech database. In the detection experiments, the results revealed that the best performance was obtained using the embeddings from the first layer of the wav2vec model that yielded an absolute improvement of 1.23% in accuracy compared to the best performing baseline feature (spectrogram). In the studied severity level classification task, the results revealed that the embeddings from the final layer gave an absolute improvement of 10.62% in accuracy compared to the best baseline features (mel-frequency cepstral coefficients).

Regulation of vocal intensity is a fundamental phenomenon in speech communication. Vocal intensity can be quantified using sound pressure level (SPL), which can be measured easily by recording a standard calibration signal with speech and by comparing the energy of the recorded speech signal with that of the calibration tone. Unfortunately, speech recordings are mostly conducted without the SPL calibration signal, and speech signals are saved to databases using arbitrary amplitude scales. Therefore, neither the SPL nor the intensity category (e.g. soft or loud phonation) of a saved speech signal can be determined afterwards. Even though the original level information of speech is lost when the signal is presented on arbitrary amplitude scales, the speech signal contains other acoustic cues of vocal intensity. In the current study, we study machine learning and deep learning -based methods in automatic classification of vocal intensity category when the input speech is expressed using an arbitrary amplitude scale. A new gender-balanced database consisting of speech produced in four vocal intensity categories (soft, normal, loud, and very loud) was first recorded. Support vector machine and deep neural network (DNN) models were used to develop automatic classification systems using spectrograms, mel-spectrograms, and mel-frequency cepstral coefficients as features. The DNN classifier using the mel-spectrogram showed the best classification accuracy of about 90%. The database is made publicly available at https://bit.ly/3tLPGRx.

Automatic detection of voice disorders from acoustic speech signals can help to improve reliability of medical diagnosis. However, the real-life environment in which speech signals are recorded for diagnosis can be different from the environment in which the detection system’s training data was originally collected. This mismatch between the recording conditions can decrease detection performance in practical scenarios. In this work, we propose to use a pre-trained wav2vec 2.0 model as a feature extractor to build automatic detection systems for voice disorders. The embeddings from the first layers of the context network contain information about phones, and these features are useful in voice disorder detection. We evaluate the performance of the wav2vec features in single-database and crossdatabase scenarios to study their generalizability to unseen speakers and recording conditions. The results indicate that the wav2vec features generalize better than popular spectral and cepstral baseline features.

Developing objective methods for assessing the severity of Parkinson's disease (PD) is crucial for improving the diagnosis and treatment. This study proposes two sets of novel features derived from the single frequency filtering (SFF) method: (1) SFF cepstral coefficients (SFFCC) and (2) MFCCs from the SFF (MFCC-SFF) for the severity classification of PD. Prior studies have demonstrated that SFF offers greater spectro-temporal resolution compared to the short-time Fourier transform. The study uses the PC-GITA database, which includes speech of PD patients and healthy controls produced in three speaking tasks (vowels, sentences, text reading). Experiments using the SVM classifier revealed that the proposed features outperformed the conventional MFCCs in all three speaking tasks. The proposed SFFCC and MFCC-SFF features gave a relative improvement of 5.8% and 2.3% for the vowel task, 7.0% & 1.8% for the sentence task, and 2.4% and 1.1% for the read text task, in comparison to MFCC features.

In speech communication, talkers regulate vocal intensity resulting in speech signals of different intensity categories (e.g., soft, loud). Intensity category carries important information about the speaker's health and emotions. However, many speech databases lack calibration information, and therefore sound pressure level cannot be measured from the recorded data. Machine learning, however, can be used in intensity category classification even though calibration information is not available. This study investigates pre-trained model embeddings (Wav2vec2 and Whisper) in classification of vocal intensity category (soft, normal, loud, and very loud) from speech signals expressed using arbitrary amplitude scales. We use a new database consisting of two speaking tasks (sentence and paragraph). Support vector machine is used as a classifier. Our results show that the pre-trained model embeddings outperformed three baseline features, providing improvements of up to 7%(absolute) in accuracy.

This work was supported by the Academy of Finland (grant number 313390). The computational resources were provided by Aalto ScienceIT.