Unsupervised Paraphasia Classification in Aphasic Speech (2020)

Abstract: Aphasia is a speech and language disorder which results from brain damage, often characterized by word retrieval deficit (anomia) resulting in naming errors (paraphasia). Automatic paraphasia detection has many benefits for both treatment and diagnosis of Aphasia and its type. But supervised learning methods cant be properly utilized as there is a lack of aphasic speech data. In this paper, we describe our novel unsupervised method which can be implemented without the need for labeled paraphasia data. Our evaluations show that our method outperforms previous work based on supervised learning and transfer learning approaches for English. We demonstrate the utility of our method as an essential first step in developing augmentative and alternative communication (AAC) devices for patients suffering from aphasia in any language.

## introduction aphasia is a speech and language disorder commonly acquired by brain damage resulting from a stroke @xcite . many people around the world suffer from aphasia as there are at least 2 million patients in usa and 250,000 in great britain @xcite . anomia, the difficulty in spoken word retrieval, is a common symptom in aphasic speech @xcite . a majority of persons with aphasia (pwa) suffer from varying degrees of anomia @xcite . anomia further results in various types of paraphasia (naming errors) which impedes the pwa's ability to carry out meaningful conversation leading to loneliness and social anxiety @xcite . there are three common types of paraphasia which occur in aphasic speech, namely semantic, phonemic and neologistic @xcite . in semantic para-phasia, the pwa substitutes a semantically similar word eg. (substituting elbow with knee). in phonemic paraphasia, there are various sub types involving the type of phoneme substitution such as, substituting bat with lat, inserting or deleting a phoneme (drake as dake) or phoneme movements (candle with cancle). lastly, in neologistic paraphasia, the target word is substituted with a nonword (harmonica with parokada). detecting and classifying the type of paraphasia is useful to determine the type of aphasia and which treatment to prescribe @xcite . aphasia talkbank @xcite , is a large scale multi-modal online database of aphasic speech data. it contains aphasic speech data for many languages such as english, french etc which is primarily used by clinical researchers to study aphasia @xcite . while the amount of data is sufficient for clinical researchers, there is a lack of data to implement supervised learning methods. this is true not only for a well researched language like english, but also for low-resource foot_0 languages like greek, spanish etc. to counter the lack of data and to extend the proposed method for low-resource languages too, we investigate an unsupervised approach. we first consider large and available speech corpuses such as librispeech @xcite to create speech embeddings of individual words similar to @xcite . we then perform soft clustering using hdbscan on these embeddings, and classify each word by using simple rules with a cutoff hyperparameter. the whole method is endto-end unsupervised and can be applied to any language. in our evaluations section, we demonstrate the efficacy of our method over a naive baseline and the transfer learning method used by @xcite for english. we hope that such an unsupervised method allows for development of aac devices improving daily life of not only english-speaking pwa's but also pwa's in other languages. ## related work recently, researchers have demonstrated the use of machine learning methods not only to diagnose the type of aphasia but also to rehabilitate and treat pwa's. mainly focusing on obtaining a medical diagnosis, @xcite applied feature selection using a transcript and low-level acoustic features to classify between two sub-types of primary progressive aphasia. likewise, @xcite used speech and language features to classify between three broad types of frontotemporal lobar degeneration, including progressive nonfluent aphasia. further, given speech samples of pwa's, @xcite @xcite proposed approaches for predicting the utterance-level pronunciation and prosody scores. @xcite aimed to tackle the contextually similar problem through keyword spotting. it recognized target words from phrases spoken by the pwa but disregarded finegrained word-level errors such as paraphasias. deep learning methods to detect paraphasia was first demonstrated in @xcite . it worked around the notion of mispronunciation detection, adopting the methods of @xcite , which used dynamic time warping (dtw) features to provide a quantitative comparison of word and phone-level pronunciations between native and non-native speakers. similarly, @xcite has used dtw and other acoustic features like phone edit distance and goodness of pronunciation, to distinguish between target transcripts and paraphasias. consequently, it has also used automatic speech recognition (asr) techniques to generate the target transcripts from the paraphasias automatically. in the end, all of these proposed methods require target transcripts for their core functioning. to the best of our knowledge, no existing work provides an unsupervised approach to detecting and classifying paraphasia from aphasic speech. in this paper, we explore a realistic scenario where we have access only to the free form discussion with pwa's. ## method aphasic speech data can be collected in mainly two ways: as a free form discussion between a pwa and an interviewer or a pwa reading a set of provided scripts. while a pwa reading from scripts is conducive to supervised learning methods, it is rarely the case in real life. hence, our goal is to perform paraphasia detection and classification in the wild i.e. without any target scripts. another motivation for classification in the wild is the lack of labeled english aphasic speech data. further, the available speech data has a class imbalance (phonemic and neologistic paraphasias account for 12.0 and 6.4 percent respectively). low-resource languages such as hindi, greek etc. have a serious lack of aphasia speech data and almost non-existent labeled speech data. using transfer-learning approaches similar to @xcite , would not allow extending it to such low-resource languages. hence, it was necessary to investigate unsupervised approaches for paraphasia classification. in this section, we outline our proposed unsupervised method which consists of first creating speech embeddings of non-aphasic speech data and then performing soft clustering to further classify the type of paraphasia detected. ## evaluation in order to validate the claims made in the previous section, we perform the following evaluations. for a fair comparison, we use the same test dataset used in @xcite , and perform further analysis on our soft clustering approach. in this section, we detail the experimental setup used including the model structure and hyperparameters, the metrics and the baselines used to compare and finally expand on the results of our method. ## conclusion the work presented in this paper is heavily inspired by @xcite , but differs and improves it in the following ways. we provide a completely unsupervised method which outperforms previous work in paraphasia classification and detection. while we maintain that our method can be used for all languages, irrespective of aphasic speech data, due to time constraints we could include only english in our evaluations. we lay the ground-work for paraphasia classification in low-resource languages allowing for development of asr and aac systems for not only english-speaking pwa's but also pwa's in developing nations. our future work will target demonstrating the method on other languages. we also hope to address semantic paraphasia in future work and create, deploy aac systems building on the method proposed in this paper.