# Introduction to Speech Processing : Paralinguistic speech processing

Created by Okko Räsänen, last modified on Oct 13, 2020

Paralinguistic speech processing (PSP) refers to analysis of speech signals with the aim of extracting information beyond the linguistic content of speech (hence paralinguistic = alongside linguistic content; see also Schuller & Batliner, 2014). In other words, PSP does not focus on what is the literal transmitted message but on what additional information is conveyed by the signal. Speaker diarization, recognition, and verification, even though focusing on non-linguistic aspects, are also traditionally considered as separate problems that do not fall within the scope of PSP. A classical example of PSP is speech emotion recognition, where the aim is to infer the emotional state of a speaker based on a sample of his or her speech. In a similar manner, information related to the health or age of a speaker could be inferred from the speech signal.

### Coupling between speaker states and the speech signal

The basic starting point for PSP systems is that the speech signal also reflects the underlying cognitive and neurophysiological state of a speaker. This is since speaking involves highly complicated cognitive processing in terms of real-time communicative, linguistic, and articulatory planning. In addition, execution of these plans requires highly-precise motor control of articulators paired with real-time monitoring of the resulting acoustic signal, and both of these tasks take place in parallel with further speech planning. The mental state of the speaker may also affect the manner that the speaker wishes to express himself or herself. Finally, the overall physiological characteristics of the speech production apparatus also shape the resulting signal, and details of these characteristics may also change due to illnesses or habits. This means that many temporary or permanent perturbations in the cognitive and physical machinery of a talker may show up in the resulting speech.

To give some examples, substantial cognitive load (e.g., a concurrent attention-requiring task) or neurodegenerative diseases affecting memory (e.g., Alzheimer's disease) may impact speech planning due to compromised cognitive resources, resulting in speech output that differs from the typical speech from the same person in non-stressful or healthy conditons. In the same way, neurodegenerative diseases affecting the brain's motor system (e.g., Parkinson's disease) may impact fluidity and clarity of speech production, and the symptoms will become more pronounced as the disease progresses. As for articulatory changes, stress and emotional distress can cause increased tension in the muscles of the larynx, which can result in tightening of the vocal folds and therefore also causes increases in the fundamental frequency of speech. Changes in the physical characteristics of the vocal tract may result from, e.g., having a cold. In this case, mucus on tract surfaces may affect resonance and damping characteristics of the vocal tract. In addition, the mucus may prevent full closing of the velum, causing nasalized speech often associated with a severe cold. The speaker may also speak differently due to cognitive fatigue and throat soreness due to the cold. Aging will also change the characteristics of the speech production apparatus, not only in childhood but also in later years of life. These changes are driven by physiological changes in the glottis and in the vocal tract, where growth of the vocal tract length in early childhood has an especially pronounced effect. The voice change in puberty is also an example of quick growth of the larynx and vocal folds, but small changes in the vocal folds and their control may also take with later aging.

In addition to information that is not directly related to intended communicative goals, speech also contains paralinguistic characteristics related to communication. This is because speech has co-evolved with the development of other social skills in humans over thousands of years. Speech (and gestures) can thefore play different types of social coordinative roles beyond the literal linguistic message transmitted. For instance, prosody, and speaking style in general, can reflect different social roles such as submissiveness, arrogance, or authority in different interactions. Attitudes and emotions showing up in speech can also be considered as communicative signals facilitating social interaction and cohesion, not just being speaker-internal states that inadvertently "leak out" for others to perceive. Demonstration of anger or happiness through voice can transmit important information regarding social dynamics even when visual contact between the interlocutors is not possible. As a concrete example, consider having a telephone conversation with someone close to you without access to anything else than the literal message (e.g., substituting the original speech with monotone but perfectly intelligible speech synthesis) while trying to communicate highly sensitive and important personal information.

The basic aim of PSP is to use computational means to understand and characterize the ways that different paralinguistic factors shape the speech signal, and to build automatic systems for analyzing and detecting the paralinguistic factors from real speech captured in various settings.

Also note that the distinction between PSP systems and other established areas of speech processing is not always clear-cut. For instance, automated methods for speech intelligibility assessment are also focusing on extralinguistic factors, and the task of speaker recognition was already mentioned at the beginning of this section. In addition, more flexible control of speaking style is an ongoing topic of research in speech synthesis, where the research focus is gradually changing from the production of high-quality to speech to creation of systems capable of richer vocalic expression. However, there is no need for a strict distinction of PSP from other types of processing tasks, but PSP can be viewed as an umbrella term for the increasingly many analysis tasks focused on the various non-literal aspects of spoken language, and where similar data-driven methodology is usually applicable across a broad range of PSP phenomena (see also Schuller & Batliner, 2014, for a discussion).

### Speaker traits and states

Schuller & Batliner (2014) use a distinction into two types of speaker characteristics: traits and states. These are related to the temporal properties of the analyzed phenomena. Speaker traits are long-term and slowly-changing characteristics of the speaker, such as personality traits (e.g., Big Five classification), gender, age, or dialect. On the other hand, speaker states are short- to medium-term phenomena, such as speaker's emotional state, attitude in a conversation, (temporary) health conditions, fatigue, or stress level. When collecting data for PSP research and system development, it is important to consider the time-scale of the phenomenon to be analyzed and how this relates to practical needs of the analysis task (e.g., how much speech can be collected and analyzed before classification decision; does the system have to be real-time). For instance, quickly changing characteristics such as emotional state should be analyzed from relatively short speech recordings where the factor of interest can be assumed to be stable. For example, recognition of speaker's emotional state during a single utterance is a widely adopted approach in speech emotion recognition. In contrast, analysis of speaker health (e.g., COVID-19 symptoms) from just one utterance is likely to be inaccurate, but speaker-dependent data collection and analysis across longer stretches of speech will likely produce more reliable analysis outcomes. Moreover, longitudinal monitoring of a subject across longer periods of time is likely to be more accurate in detecting changes in the speaker's voice, as the system can be adapted to the acoustic and linguistic characteristics of that specific speaker. For instance, subject-specific monitoring of the progression of a neurodegenerative disease based on speech is likely to be more accurate than automatic classification of disease severity from a bag of utterances from a random collection of speakers.

### Typical applications of PSP

Some possible applications of paralinguistic tasks include, but are not limited to:

- Emotion classification
- Personality classification (e.g., Big Five traits)
- Sleepiness or intoxication detection
- Analysis of cognitive or physical load
- Health-related analyses (cold, snoring, neurodegenerative diseases etc.)
- Speech addressee analysis (e.g., adult- vs. infant-directed speech)
- Age and gender recognition
- Sincerity analysis
- Attitude analysis

### Basic problem formulation and standard solutions

The basic goal of paralinguistic analysis is to extract paralinguistic information of interest while ignoring the signal variability introduced by other factors, such as linguistic content, speaker identity, background noise or transmission channel characteristics (aka. nuisance factors). However, for some tasks, it may also be useful to analyse the lexical and grammatical content of speech in order to infer information regarding the phenomena of interest.

Typical PSP systems follow the two classical tasks of machine learning: classification and regression. In classification, the goal is to build a system that can assign a speech sample (e.g., an utterance) into one of two or more categories (e.g., intoxicated or not intoxicated). In regression, the target is a continuous (or at least ordinal) measure (e.g., blood alcohol concentration percentage).

Fig. 1 illustrates a standard PSP system pipeline, which follows a typical supervised machine learning scenario. First, a number of features are extracted from the speech signal. These features, together with the corresponding class labels, are then used to train a classifier model for the training dataset. During testing and actual use of the system, the classifier is used to determine the most likely class of an input waveform. Depending on the classifier architecture, the classification result may or may not be associated with a confidence measure of the classification decision. In regression tasks, the process is otherwise the same, but the categorical class labels are replaced by continous-valued measures and the classifier is replaced by a regression model.

A central characteristic of many PSP tasks is that the analyzed phenomenon (e.g., speaker emotion) is assumed to be fixed at a certain time-scale, such as across one utterance, and therefore classification decisions should also be made at time-scales longer than typical frame-level signal features. In this case, it would desirable to obtain a fixed-dimensional feature representation of the signal even if the duration of the input waveform varies from case to case. This can be achieved by a two-step process: 1) first extracting regular frame-level features (e.g., spectral features such as FFT) with high temporal resolution (e.g., one frame every 10-ms), sometimes referred to as low-level descriptors (LLDs), and then 2) calculating statistical parameters ("functionals") of each of the features across all the time-frames (illustrated in Fig. 2). Typical functionals of LLDs include, e.g., min, max, mean, variance, skewness, and kurtosis, but they can also be measures, such as centroids, percentiles, or different types of means. In order to acquire a more complete picture of the signal dynamics, first- and second-order time-derivatives of the frame-level features ("deltas" and "delta-deltas") are often included in the feature set before calculating the functionals. In neural networks, a varying-length signal can also be represented by a fixed-dimensional embedding extracted from the input waveform, such as taking the output of an LSTM-layer. Once the input signals are represented by fixed-dimensional feature vectors, standard machine learning classifiers can be applied to the data.

Figure 1: An example of a classical PSP processing pipeline with training (top) and usage (bottom). Speech features are first extracted from the original speech waveform, followed by a classifier that has been trained using supervised learning with labeled training samples.

Figure 2: An example signal-level feature extraction process where variable-duration utterances become represented by fixed-dimensional feature vectors, consisting of statistical parameters ("functionals") calculated across frame-by-frame speech features (sometimes also referred to as low-level descriptors, or "LLDs").

In the both cases of classification and regression, the challenge is to build a system that can discriminate the key features of the phenomenon without being affected by the other sources of signal variability. High-performance well-generalizing systems can be approached with three basic strategies: well-designed features, robust classifiers, or end-to-end learning, where signal representations ("features") and classifiers are jointly learned from the data. Selection of the approach depends on two primary factors: domain knowledge and availability of representative large-scale training data (Fig. 3).

The basic principle is that well-designed or otherwise properly chosen features require less training data for inference of the machine learning model parameters, but this necessitates that the acoustic/linguistic characteristics of the analyzed phenomenon are known in order to design features that capture them. If there are only limited data and limited domain knowledge, it is still possible to calculate a large number of potentially relevant features and then use a classifier such as Support Vector Machines (SVMs; Boser et al., 1992) that can robustly handle high-dimensional features, including potentially task-irrelevant or otherwise noisy features. For instance, the baseline systems for annual Computational Paralinguistic Challenges (see also below) have traditionally used a combination of more than 6000 signal-level features together with an SVM classifier, and often these systems have been highly competitive with other solutions. Alternatively, feature selection techniques may be applied in conjunction with data labeling to find a more compact feature set for the problem at hand (see, e.g., Pohjalainen et al., 2015).

If there are plenty of training data available, multilayer neural networks can be used to simultaneously learn useful signal representations and a classifier for the given problem. This type of representation learning can operate directly on the acoustic waveform or using some standard spectral representation with limited additional assumptions, such as FFT or log-Mel spectrum. The obvious advantage is that feature design is no longer needed, and the features and the classifier are seamlessly integrated and jointly optimized. In addition, even if domain knowledge would be available, the assumptions built into manually tailored features may lose some details of the modeled phenomenon, whereas end-to-end neural networks can potentially use all the information available in the input signal. Similarly to many other applications of machine learning, neural networks can therefore be expected to outperform the "more classical" approaches if sufficient training data are available for the task (see also sub-section below for data in PSP). However, besides the data requirement drawback, the standard problems and principles of neural network design and training apply, including the lack of access to the global optimum during the parameter optimization process.

If sufficient training data and domain knowledge are both available, one may also combine representation learning with good initial features or some type of model priors or constraints. This may speed-up the learning process or improve model convergence to a more effective solution due to a more favorable starting point for the optimization process. One particularly interesting new research direction is the use of differentiable computational graphs in feature extraction. In this case, prior task-related knowledge could be incorporated into digital signal processing (DSP) steps used to extract some initial features, but the feature extraction algorithm would be implemented together with the rest of the network as one differentiable computational graph (e.g., using a framework such as TensorFlow). This would allow error backpropagation through the entire pipeline from classification decisions to the feature extractor, thereby enabling task-optimized adaptation of the feature extraction or pre-processing steps. As an example, Discrete Fourier Transform is a differentiable function, allowing error gradients to pass through it, although by default it does not have any free parameters to optimize. However, similar signal transformations but with parametrized basis functions could be utilized and optimized jointly with the rest of the model.

Figure 3: Basic strategies for PSP system development given the two main considerations: availability of training data (x-axis) and domain knowledge (y-axis).

Naturally, the division into the four categories described in Fig. 3 is by no means definite. For instance, recent developments in self-supervised representation learning (van den Oord et al., 2018; Chung et al., 2019; Baevski et al., 2020) or multitask learning (e.g., Wu et al., 2015) are still largely unexplored in PSP. Such approaches could enable effective utilization of large amounts of unlabeled speech data with fewer labeled examples in the target domain of interest.

### Data collection and data sparsity

Since modern PSP largely relies on machine learning, representative training data will be required for the phenomenon of interest. However, access to high-quality labeled data is often be limited for many PSP tasks. First of all, the data collection itself is often challenging and may include important ethical considerations, such as collecting data from intoxicated speakers or subjects with rare diseases, or data where some type of objective (physiological) measurements of emotional state are captured simultaneously with the speech audio. Availability of reliable ground-truth labels for the speech data can also be difficult. For instance, there is no direct way to measure the underlying emotional states of speakers, whereas induced emotional speech by professional actors may not properly reflect the variability of real-world emotional expression. In a similar manner, assessment of severity of many diseases is based on various indirect measurements and clinical diagnostic practices, not on some type of oracle knowledge on a universally standardized scale. Every time humans are used for data labelling (e.g., assessing emotions), there is a certain degree of inter-annotator inconsistency due to differing opinions and general variability in human performance. This is the case even when domain experts are used for the task. Naturally, the more difficult the task or more ambiguous the phenomenon, the more there will be noise and ambiguity in the labeling.

Another limitation hindering PSP progress is that many PSP datasets cannot be freely distributed to the research community due to data ownership and human participant privacy protection considerations. As a clear example, speech with metadata related to factors such as health or IQ of the speakers is highly sensitive in nature, and not all speakers consent to open distribution of their identifiable voice together with such private data of themselves. The data ownership considerations inherently limit the pooling of different speech corpora in order to build more comprehensive databases of speech related to different phenomena of interest, and generally slow down replicable open science. On the other hand, it is of utmost importance to respect the privacy of human participants in PSP (or any other) research—not only due to ethical considerations, but also since the entire field depends on access to data from voluntary human participants. Commercial interests to data ownership are also often unavoidable.

In total, this means that data is often a limiting factor in system performance, and therefore deep neural networks have still not become the only off-the-shelf-solution for many PSP problems. Efforts for more flexible (but ethical) data sharing and pooling is therefore an important challenge for future research. More powerful technical solutions and deeper understanding of the PSP phenomena could be acquired by combining rich data (and metadata) from various sources, including different languages, cultural environments, and recording conditions.

### Computational Paralinguistic Challenge

Research in PSP has been strongly advanced by an annual Computational Paralinguistic Challenge (ComParE) held in the context of ISCA Interspeech conferences (see http://www.compare.openaudio.eu/). Every year since 2009, ComParE has included a number of paralinguistic analysis tasks with pre-defined datasets in which participants can compete with each other. Competitive baseline systems, evaluation protocols and results are always provided to the participants as a starting point, enabling low-barrier access to the world of PSP for researchers with various backgrounds. In addition, new tasks and datasets can be proposed to challenge organizers, providing a useful channel for data owners and researchers to obtain competitive solutions to their analysis problems.

## Further reading and materials on PSP

Schuller, B. et al.: Computational Paralinguistic Challenge. WWW-site: http://www.compare.openaudio.eu/, last accessed 11th October 2020.

Eyben, F., Wöllmer, M., & Schuller, B. (2010). openSMILE -- The Munich Versatile and Fast Open-Source Audio Feature Extractor. Proc. 18th ACM International Conference on Multimedia, Florence, Italy, pp. 1459–1464, https://www.audeering.com/opensmile/

Pohjalainen J., Räsänen O., & Kadioglu S. (2015). Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits. Computer Speech and Language, 29, 145–171.

Schuller, B. & Batliner, A. (2014). Computational paralinguistics: Emotion, affect and personality in speech and language processing. John Wiley & Sons Ltd, UK.

## Other references

Baevski, A., Zhou, H., Abdelrahman, M., & Auli, M. (2020). wav2vec 2.0: A framework for self-supervised learning of speech representations. arXiv pre-print, https://arxiv.org/abs/2006.11477

Chung, Y-A., Hsu W-N., Tang. H., & Glass, J. (2019). An unsupervised autoregressive model for speech representation learning. Proc. Interspeech-2019, Graz, Austria, pp. 146—150.

Boser, B., Guyon, I., & Vapnik, V. (1992). A training algorithm for optimal margin classifiers. Proc. 5th Annual Workshop on Computational Learning Theory, pp. 144–152.

van den Oord, A., Li, Y., & Vinyals, O. (2018). Representation learning with contrastive predictive coding. arXiv pre-print, https://arxiv.org/abs/1807.03748.

Wu, Z., Valentini-Botinhao, C., Watts, O., & King, S. (2015). Deep neural networks employing multi-task learning and stacked bottleneck features for speech synthesis. Proc. ICASSP-2015, Brisbane, Australia, pp. 4460–4464.

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