Iscribe dictarion11/16/2023 ![]() ![]() But he didn't want to use underground bomb shelters. ![]() "I never used basements or anything like that," he says, "because it's damp down there. Krasnoshchok, who's 41, describes himself as a "geopolitical surrealist" painter. The Viterbi algorithm is used to segment the word sequence into dictation and conversation regions, allowing transitions within a speech segment.Ĭombining all features using a Random Forest: Here all the above features are combined using a random forest classifier.Once the war started, he wanted to document the way the invasion dramatically changed the country. The transition probabilities were biased to remain in the same state (0.9). It models the text generated as an HMM-process with two states – conversation, dictation. Hidden-Markov-Model (HMM)-Conditioned LM: This method also uses ASR hypotheses. Language Models (LM) likelihood ratio: Here the goal is determined by which LM, one generated from clinical text or one generated from conversational text, best explains the text seen. Examples include: “start dictation,” comma,” period,” ”chief complaint,” “assessment and plan” and “new paragraph.” Any audio segment that contained one of these types of keywords was labeled as “dictation.” Rule-based approach using ASR hypotheses: The approach here is to look for text with high confidence that correspond to what physicians typically use for dictation. We used speech detection to identify speech segments that were separated by at least one second of no speech. Feature set included root mean square (RMS), fundamental frequency, zero-crossing rate, chroma short-term Fourier transform (STFT), spectral centroid, spectral bandwidth, spectral rolloff, and spectral flatness. Random Forest: Acoustic features only: In this experiment a random forest model was constructed using acoustic features extracted using the Librosa library. These are briefly discussed here (for fuller treatment see the paper). Five different ways to segment the audio into conversation and dictation regions were implemented. To ground progress we used two Chance Classifiers labeling all audio either as conversational or dictation, that serve as baselines for our experiments. Average results across all physicians are then reported. In essence, train on audios associated with 20 physicians (~80 audio files) and test on left out physician. The test protocol employed was leave-one-physician-out cross-validation. ![]() Ground truth labels are provided by manual annotation. CER measures how much time of the total recording is misclassified, while F1 is calculated as the harmonic mean between recall and precision of the model on the dictated regions. To evaluate the segmentation classification model that we developed, we used two metrics: classification error rate (CER) and F1 score to evaluate performance of all our models. The segmentation created a new audio segment when no speech was detected for 1.0 seconds or more. The recognition results were based on interpolating two language models one trained on a mix of clinical note dictations and another trained on conversational speech and text data. The context for our work discussed here is supporting asynchronous scribing.Ī Time Delay Neural Network (TDNN) automatic speech recognition (ASR) model was trained on handheld microphones and mobile devices. In the asynchronous mode, a recording of the conversation is sent to the scribe to generate documentation after the visit. In synchronous mode the scribes are virtually present and can interact with the physician. We look at two different modes: synchronous and asynchronous. In the right, a scribe is involved in listening and documenting the visit. In the left, it shows the physician documenting, while largely ignoring the patient. The figure below is illustrative of remote scribing processes. We focus our solutions on technology assist for remote scribes. Medical scribes can be physically present during the encounter or be remote. ![]() One approach to reduce the burden of documentation and to limit the physicians’ interactions with EHR systems is to shift some of these responsibilities to medical scribes. Although EHR systems allow fast access to patient information, documenting in these systems is often quite complex and time intensive from a physician perspective.Ī growing body of research also links this documentation process within EHR systems to physician burnout. The widespread adoption of electronic health record (EHR) systems has changed the workflow of doctors dramatically. In this paper, published in 2021 IEEE Automatic Speech Recognition and Understanding Workshop, we explore the feasibility of detecting embedded dictations in recordings of doctor-patient conversations. ![]()
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