DRAFT - Mental Health Diagnosis, Prediction, and Treatment Plan Evaluation Using Speech Features

Objective
The goal of this project is to leverage speech and voice analysis techniques to enhance mental heath diagnosis, predict treatment outcomes, and evaluate the effectiveness of treatment plans. By analyzing speech features extracted from audio recordings, we aim to develop objective measures for assessing mental health status and treatment response.

Workflow Overview

Data Collection and Preprocessing
Speaker Diarization
Feature Extraction
Speech Signal Exploratory Analysis
Model Development for Diagnosis and Prediction
Treatment Plan Evaluation
Results and Analysis
Discussion and Interpretation
Conclusion

Data Collection and Preprocessing


Data Sources

The source of speech and voice data to be used in the analysis are audio files derived from the team members. Future speech and voice data sources will include clinician patient session and will include data such as patient mental health diagnosis, clinical scales such as PHQ-9, etc. treatment history, genomic data , EHR data and demographic characteristics.

Preprocessing Steps

The following preprocessing steps will be applied to patient audio files:
Noise Removal
Normalization
Segmentation
Treatment session annotation
Treatment Adherence


Feature Extraction

Extracted Features

Clinical Relevance to Mental Health Disorders and Neurological Disorders

The following speech and voice features extracted from audio recordings hold significant clinical relevance, particularly in the context of mental health disorders such as anxiety, depression, bipolar I or II, schizophrenia, and neurological disorders including traumatic brain injury (TBI), Alzheimer's disease, Parkinson's disease, and congestive heart failure:
Duration: Variations in speech duration may indicate changes in speech fluency, which can be relevant for assessing cognitive processing speed and verbal expression in individuals with neurological disorders such as TBI or Alzheimer's disease.
Variations in speech duration may be indicative of certain mental health disorders, such as depression (slowed speech) or mania (rapid speech).
Intensity/Volume: Changes in speech intensity may reflect alterations in emotional expression or vocal effort, potentially serving as markers for mood disorders like depression or anxiety.
Formants: Disturbances in formant frequencies may be indicative of articulatory or phonatory dysfunction, which can occur in neurological conditions such as Parkinson's disease or dysarthria secondary to TBI.
Mel-Frequency Cepstral Coefficients (MFCCs): Differences in MFCC patterns provide insight into speech clarity or emotional expressiveness characteristic of mood and anxiety disorders. Also, alterations in MFCCs may signify changes in speech timbre, which can be relevant for monitoring speech deterioration in conditions like Alzheimer's disease or detecting emotional prosody disturbances in schizophrenia or depresssion.
Spectral Centroid: Indicates where the center of mass of the spectrum is located and can give insights into the brightness of the sound. The spectral centroid may reflect changes in voice quality or vocal tract resonance and can reveal changes in speech timbre, which may be linked to emotional states or cognitive processes affected by mental health disorders.
Spectral Flux: Increased spectral flux may indicate sudden shifts in vocal behavior or emotional arousal, which may be observed in individuals with anxiety disorders or during manic episodes in bipolar disorder.
Zero-Crossing Rate: Variations in zero-crossing rate can reflect changes in speech rate or phonetic content, offering insights into cognitive processing speed and linguistic fluency, particularly relevant in conditions like schizophrenia or Alzheimer's disease.
Energy Envelope: Changes in the energy envelope may signify shifts in vocal effort or emotional intensity, which can be relevant for assessing affective symptoms in depression or detecting vocal fatigue in Parkinson's disease patients.
Pitch Strength: Alterations in pitch strength may be indicative of changes in vocal pitch or intonation patterns, which can serve as markers for mood disorders like depression or schizophrenia.
Spectral Roll-off: Spectral roll-off can provide insights into the spectral shape of the speech signal, which may be relevant for detecting changes in vocal resonance or speech clarity. Alsoi ndicates how noise-like or tonal a sound is. Spectral flatness analysis can detect changes in speech quality, such as the presence of background noise or distortions, which may impact speech intelligibility and communication effectiveness. Monitoring spectral flatness can be useful in assessing treatment outcomes for speech disorders or evaluating interventions targeting speech clarity.
Spectral Flatness: Changes in spectral flatness may reflect alterations in voice quality or the presence of noise interference, which can impact speech intelligibility in individuals with dysphonia or speech disorders related to neurological conditions.
Harmonics-to-Noise Ratio (HNR): Disturbances in HNR may indicate vocal fold vibratory abnormalities, relevant for voice quality assessment in conditions like Parkinson's disease or dysphonia associated with TBI. HNR analysis quantifies vocal fold vibration and can indicate vocal fold pathology or irregularities in voice production. Changes in HNR may be associated with voice disorders, such as vocal nodules or vocal fold paralysis, which can occur in individuals with mental health disorders or neurological conditions.
Jitter and Shimmer: Increased jitter or shimmer may signify vocal instability or dysphonia, observed in conditions such as dysarthria secondary to TBI or vocal cord dysfunction in congestive heart failure patients. These speech features are also relevant for mood and anxiety disorders
Prosody: Prosodic features can convey emotional expression, linguistic emphasis, and pragmatic information in communication, providing insights into affective symptoms in mood disorders like depression or bipolar disorder.
Speech Rate: Variations in speech rate may indicate changes in cognitive processing, emotional state, or linguistic fluency, relevant for monitoring symptom severity in conditions like depression, schizophrenia, mania or Alzheimer's disease.
Spectral Flux: Spectral flux measures the change in spectral content over time and can capture variations in vocal energy and dynamics. Increased spectral flux may be associated with emotional arousal or variability in speech production, making it potentially relevant for assessing anxiety, bipolar disorder, depression, and schizophrenia.
Spectral Entrophy: Spectral entropy is a feature derived from the spectral characteristics of audio signals, which measures the degree of disorder or unpredictability in the distribution of spectral energy. In the context of mental health disorders such as anxiety, bipolar I, bipolar II, depression, and schizophrenia, spectral entropy may have clinical relevance in the following ways:
1. Anxiety: Increased levels of anxiety are often associated with physiological arousal and heightened sympathetic nervous system activity. In speech signals, anxiety may manifest as increased vocal tension, resulting in alterations in spectral entropy. Higher spectral entropy values in speech signals of individuals with anxiety may indicate increased variability or unpredictability in vocal characteristics, reflecting underlying emotional distress.
2. Bipolar I and Bipolar II: Bipolar disorders involve periods of manic or hypomanic episodes alternating with depressive episodes. Spectral entropy of speech signals may capture fluctuations in vocal characteristics associated with mood changes in individuals with bipolar disorder. During manic or hypomanic episodes, speech may exhibit higher spectral entropy due to increased speech rate, volume, and variability. Conversely, depressive episodes may be characterized by lower spectral entropy reflecting reduced speech variability and energy.
3. Depression: Depression is characterized by persistent feelings of sadness, low energy, and changes in psychomotor activity. Spectral entropy of speech signals in individuals with depression may reflect alterations in speech fluency and expressiveness. Higher spectral entropy values may be observed during periods of agitation or restlessness, while lower values may indicate reduced vocal variability and energy during depressive episodes.
4. Schizophrenia: Schizophrenia is a complex psychiatric disorder characterized by disruptions in thought processes, perception, and behavior. Spectral entropy analysis of speech signals in individuals with schizophrenia may reveal abnormalities in speech organization and coherence. Higher spectral entropy values may be indicative of disorganized or tangential speech patterns, reflecting underlying cognitive deficits and thought disorder.
Overall, spectral entropy analysis of speech signals may provide valuable insights into the vocal characteristics associated with different mental health disorders, offering potential markers for objective assessment and monitoring of symptoms.

Calculating Delta and Delta- Delta Speech Features

Calculating delta and delta-delta features is a common technique in speech processing for capturing temporal dynamics and variations in speech signals over time. These features are derived from the original speech features and provide additional information about the rate of change or acceleration of the speech signal. Here's why delta and delta-delta features are useful:
1. Temporal Dynamics: Speech signals are highly dynamic, and important information can be encoded in the changes occurring over time. Delta and delta-delta features capture the rate of change and acceleration of speech features, respectively, providing insight into how the speech signal evolves over time.
2. Contextual Information: Delta and delta-delta features provide contextual information about the local dynamics of speech segments. For example, changes in pitch, intensity, or spectral characteristics over short time intervals can convey nuances in speech such as prosody, emotion, or speaker identity.
3. Robustness: By incorporating information about temporal dynamics, delta and delta-delta features can enhance the robustness of speech processing systems to variations in speech signals due to factors such as speaker variability, background noise, or channel distortion.
4. Dimensionality Reduction: In some cases, delta and delta-delta features can help reduce the dimensionality of the feature space while preserving relevant information. By representing changes in speech features rather than absolute values, delta and delta-delta features can capture salient patterns in a more compact representation.
Overall, calculating delta and delta-delta features complements the original speech features and can improve the performance of speech processing tasks such as speech recognition, speaker identification, emotion recognition, and more. These features are particularly valuable when modeling the temporal dynamics of speech signals and capturing subtle variations that contribute to the overall meaning and context of spoken language.
These speech and voice features offer valuable insights into the neurological and mental health status of individuals, enabling clinicians to assess symptoms, monitor progression, and tailor treatment interventions for optimal patient care.
The goal is to present the results of the speech feature analysis in a visual format that enables clinicians to compare speech features among multiple patients within a cohort or over assessment sessions. By visualizing the data, the aim is to emphasize trends or patterns over time, which can assist clinicians in tracking changes in speech characteristics and monitoring the progress of treatment.

Delta and Delta-Delta Speech Features and Mental Health


In the context of mental health issues such as anxiety, depression, schizophrenia, bipolar I, and bipolar II, the calculation of delta and delta-delta features in speech signals can provide valuable insights into the manifestation and characteristics of these conditions. Here's how these features relate to mental health:
1. Emotional State and Affect: Changes in speech dynamics, captured by delta and delta-delta features, can reflect variations in emotional state and affective expression. Individuals with anxiety or depression, for example, may exhibit alterations in speech tempo, pitch modulation, and energy levels, which can be detected through these features.
2. Cognitive Impairments: Schizophrenia and bipolar disorders are associated with cognitive impairments and disruptions in thought processes. Delta and delta-delta features may reveal abnormalities in speech fluency, coherence, and syntactic complexity, which could be indicative of cognitive deficits or disorganized thinking patterns.
3. Vocal Characteristics: Bipolar disorders, particularly during manic or hypomanic episodes, may be characterized by changes in vocal characteristics such as speech rate, volume, and prosody. Delta and delta-delta features can capture these fluctuations and provide quantitative measures of vocal variability associated with mood disturbances.
4. Diagnostic Biomarkers: Research suggests that speech features, including delta and delta-delta parameters, hold promise as diagnostic biomarkers for mental health disorders. By analyzing patterns in speech dynamics, machine learning models can potentially differentiate between individuals with different mental health conditions and aid in early detection and diagnosis.
5. Treatment Monitoring: Monitoring changes in speech dynamics over time, including the evolution of delta and delta-delta features, may offer insights into treatment response and disease progression. Longitudinal analysis of speech signals could help track symptom severity, assess treatment efficacy, and inform personalized interventions for individuals with mental health disorders.
In summary, the calculation of delta and delta-delta features in speech signals provides a means of quantifying temporal dynamics and capturing subtle variations associated with mental health conditions. Integrating these features into computational models and diagnostic tools holds promise for improving clinical assessment and management strategies.
Feature Representation:
The extracted speech features are represented and processed for input into machine learning models as follows:
Scaling and Normalization: The speech features are scaled and normalized to ensure that they are on a comparable scale and have zero mean and unit variance. This preprocessing step is essential for improving the convergence and stability of machine learning algorithms, particularly those sensitive to feature magnitudes.
Transformation: Nonlinear transformations, such as log-transformations or power transformations, may be applied to certain features to improve their distributional properties or enhance their predictive utility. For example, logarithmic transformation of pitch strength may help mitigate skewness and improve interpretability.
Feature Engineering: Additional feature engineering techniques, such as dimensionality reduction (e.g., PCA), feature selection, or feature extraction (e.g., wavelet transforms), may be applied to extract informative features or reduce the dimensionality of the feature space. This step can help improve model generalization and interpretability.

Speaker Diarization:

Speaker Diarization from Audio Files

Steps for Speaker Diarization:

Audio Preprocessing: Preprocess the audio files to remove noise, normalize volume levels, and extract relevant audio segments for analysis.
Feature Extraction: Extract acoustic features from the preprocessed audio, such as MFCCs, spectral features, or prosodic features, to represent the speech signal.
Speaker Embedding: Transform the extracted features into speaker embeddings, which are low-dimensional representations of speaker characteristics that capture speaker-specific information.
Speaker Diarization: Perform speaker diarization using clustering algorithms or Siamese networks to partition the audio into segments corresponding to different speakers. Siamese networks are particularly effective for speaker diarization tasks as they can learn speaker similarity directly from the data.
Speaker Labeling: Assign speaker labels to the diarized segments based on the identified speakers' embeddings or clustering results.
Post-processing: Refine the speaker diarization results through post-processing techniques, such as smoothing or speaker turn-taking modeling, to improve accuracy and coherence.

Speech Signal Exploratory Analysis


Workflow for EDA Analysis of Speech Features

Data Loading and Inspection:
Load the extracted speech features dataset.
Inspect the structure of the dataset, including the feature names, data types, and summary statistics.
Data Cleaning and Preprocessing:
Handle any missing or erroneous values in the dataset.
Normalize or scale the features if necessary to ensure comparability.
Univariate Analysis:
Perform univariate analysis of each speech feature:
Calculate descriptive statistics such as mean, median, standard deviation, etc.
Visualize distributions using histograms, box plots, or kernel density plots.
Check for outliers and anomalies.
Bivariate Analysis:
Explore relationships between pairs of speech features:
Calculate pairwise correlations to identify strong associations.
Visualize scatter plots or heatmaps to depict correlations.
Investigate potential multicollinearity issues.
ANOVA Analysis:
Conduct Analysis of Variance (ANOVA) to assess the impact of categorical variables on speech features:
Identify relevant categorical variables such as diagnostic categories or treatment groups.
Perform ANOVA tests to determine if there are statistically significant differences in speech features across categories.
Visualize results using bar plots or box plots to compare group means.
State-of-the-Art Speech Feature Analysis:
Explore advanced techniques for analyzing speech features:
Apply machine learning algorithms or deep learning models to identify patterns or clusters within the speech feature dataset.
Use dimensionality reduction techniques such as PCA or t-SNE to visualize high-dimensional feature spaces.
Investigate recent research findings or methodologies in speech feature analysis and apply relevant approaches to the dataset.
Interpretation and Insights:
Interpret the findings from the EDA analysis:
Summarize key observations and trends in the data.
Discuss the implications of significant ANOVA results in the context of mental health diagnosis or treatment outcomes.
Draw insights from the speech feature analysis techniques and their potential applications in mental health research and outcomes.
Conclusion and Next Steps:
Conclude the EDA analysis by summarizing the main findings and implications.
Outline potential next steps for further analysis, such as feature selection, model development, or validation studies.

Model Development for Diagnosis and Prediction

Data Splitting

Describe how the data was split into training, validation, and test sets for model training and evaluation.

Model Selection

Outline the selection of machine learning algorithms and techniques used for mental health diagnosis and prediction. Consideration of models such as Siamese networks, Bayesian causal models, SVM, XGBoost, DNN, RNN, and CNN may be included here.

Hyperparameter Tuning

Discuss the process of tuning model hyperparameters to optimize diagnostic accuracy and prediction performance.

Evaluation Metrics

Specify the evaluation metrics used to assess model performance for mental health diagnosis and prediction, including sensitivity, specificity, Matthew’s Coefficient, Brier score, ROC-AUC, confusion matrix, per fold cross-validation scores, R2 and F1 score.

Treatment Plan Evaluation

Treatment Plan Annotation

Detail the process of annotating treatment sessions or monitoring treatment adherence using speech and voice data. Discuss any challenges or considerations for integrating speech analysis into treatment plan evaluation.

Outcome Measures

Define outcome measures used to evaluate the effectiveness of treatment plans, such as changes in speech features over time, symptom severity scores, or patient-reported outcomes.

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