SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Training Details: We perform cross-validation six times to characterize the variability in performance. We fixed the random seed to 233. For each training batch, we randomly select 16 dysphonic and 16 healthy voice recordings of the same gender from the SVD dataset; 8 healthy subjects from the mPower dataset, and 2 consecutive days' voice recordings for each subject. The SGD optimizer is used with a learning rate of 0.001 and other default settings. We use one NVIDIA Titan Xp graphic card to train our models. We train the model for 20k steps, which takes approximately 16 hours under our configurations.
Baseline Methods: We comapre against four baselines: (1) Zhang et al. [62]; (2) Harar et al. [18], which is based on a recurrent convolutional neural network model; (3) Verde et al. [53], which used a conventional features set with different classical machine learning classifiers; (4) Huckvale et al. [22], which uses the ComPare feature set from the OpenSMILE toolkit with the SVM and neural networks methods. All baseline methods are rebuilt and trained on our data using the same procedures as they outlined.
Evaluation Metrics: We use balanced accuracy to evaluate the dysphonic voice classification accuracy:
$$
\text {B a l a n c e d} = \frac {1}{2} \times \left(\frac {\mathrm {T P}}{\mathrm {T P} + \mathrm {F N}} + \frac {\mathrm {T N}}{\mathrm {T N} + \mathrm {F P}}\right). \tag {16}
$$
We use the ICC to evaluate the repeatability of our trained voice feature embeddings and other baseline methods' features. For a fair comparison, we evaluate the repeatability by using ALS dataset, which is unseen to all methods during training.
C.1 Why use a two-branch structure?
We use a two-branch structure for optimizing the dysphonic sensitivity and repeatability of embeddings simultaneously: the top embeddings generated by the encoder are further transformed to repeat-constrained embeddings and voice feature embeddings instead of directly using top embeddings for repeatability constraints and dysphonic voice detection.
We have tested three models with different structures in this experiment, including: (1) one-embedding output, i.e., Zhang et al. networks structure without modification and there is only one embeddings output can be optimized as shown in Figure C.3; (2) two-embedding output, i.e., add one MLP network to transform the top embeddings to the voice feature embeddings and there are two embeddings outputs can be optimized as shown in Figure C.4; (3) a two-branch structure (three-embedding output).
Figure C.3: The diagram of one-embedding output model structure. This is not used for any results in this paper.
Figure C.4: The diagram of two-embedding output model structure. This is not used for any results in this paper.
Convergence problems with the architecture from Figure C.3: During experiments, we noticed that if the model has only one output, there is a conflict between the ICC regularizer for repeatability
and contrastive loss for dysphonic voice detection, i.e., the model has difficulties converging. We believe this problem is because the optimization objectives of the two components are so different. The ICC regularizer focuses on the repeatability within the subject; however, the contrastive loss focuses on the dysphonic sensitivity between the healthy and dysphonia groups. The inconsistent range of these two objectives requires careful fine-tuning of the ratio between the ICC regularizer and contrastive loss. We did not find a good loss ratio, so we abandon this structure.
Convergence problems with the architecture from Figure C.4: After the one-embedding output structure failed, we considered letting the ICC regularizer and contrastive loss optimize two different embeddings, and then we tested the two-embedding output structure (Figure C.4). The encoder's output is the top embeddings, and the MLP network is used to learn dysphonic voice feature embeddings. Then the ICC regularizer regularizes the repeatability of top embeddings, and the contrastive loss regularizes the dysphonic sensitivity of voice feature embeddings. The intuition is that since the dysphonic voice feature embeddings are a function of the top embeddings, the voice feature embeddings will also have the property of high repeatability. However, this structure failed to converge. The two-embedding output structure did not solve the conflict between the two loss's optimization objectives.
Two-branch structure solves the convergence problems: After the one- and two-embedding output structures failed, we designed the two-branch (three-embedding output) structure, solving the convergence problem. The top embeddings generated by the encoder are further converted to repeatable embeddings and dysphonic voice feature embeddings using two MLP networks. The ICC regularizer regularizes the repeatability of repeatable embeddings, and the contrastive loss regularizes the dysphonic sensitivity of voice feature embeddings. Due to the repeatable and dysphonic voice feature embeddings being independently transformed from the top embeddings, the top embeddings are endowed with both properties. These are the embeddings we used in the paper.
D Summary of Used Databases in Paper
VoxCeleb 1 [37]: A large-scale speaker recognition dataset consisting of short video clips from YouTube. It includes over 100,000 utterances from more than 1,200 celebrities across various professions and demographics.
VoxCeleb 2 [11]: An extension of VoxCeleb 1, VoxCeleb 2 is an even larger dataset featuring approximately 1 million utterances from over 6,000 speakers. Together, VoxCeleb 1 and VoxCeleb 2 offer rich resources for training and evaluating speaker recognition models.
VCTK (The Voice Cloning Toolkit) [52]: VCTK is a speech dataset that includes recordings of various English accents. With over 44 hours of speech from 109 speakers, each speaking in their accent, VCTK provides a valuable resource for multi-accent speech synthesis and recognition research.
MEEI (Massachusetts Eye and Ear Infirmary): Full name is Kay Elemetrics Corp., Disordered Voice Database, Version 1.03 (CD-ROM), MEEI, Voice and Speech Lab, Boston, MA (October 1994). The MEEI Voice Disorders Database is a collection of speech samples from individuals with and without voice disorders. Participants are English speakers. It is often used in medical and clinical research to study voice pathology and develop systems to detect and analyze voice disorders. The MEEI database contains more than 1400 recordings of sustained phonations, which are collected from 53 healthy speakers and 657 speakers diagnosed with different types of dysphonia.
SVD (Saarbrücken Voice Database) [57]: The Saarbrücken Voice Database is a collection of voice recordings used for various phonetic and clinical studies. Participants are German speakers. It provides a comprehensive set of voice samples, including those from individuals with different voice disorders, aiding in the research of voice quality and characteristics. SVD database contains the voice recordings from more than 2000 speakers (428 healthy females, 259 healthy males, 727 dysphonic females, 629 dysphonic males).
HUPA (Hospital Principe de Asturias) [1]: Similar to MEEI and SVD, HUPA a collection of speech samples from individuals with and without voice disorders. Participants are Spanish speakers. HUPA contains /a/ sustained phonation recordings of 366 adult Spanish speakers (169 dysphonic and 197 healthy).