Introduction
Communication skills are one of the fundamental elements of information delivery. Embracing proper communication skills is critical to fostering a better understanding of the message with minimal ambiguity. Although different forms of communication exist, speaking remains one of the most dominant as it offers many opportunities for clarity of information (Rao (2019). Moreover, as Rao (2019) notes, speaking has become essential to modern globalization. In light of these challenges, theorists have developed numerous explanations to solve the speech challenge in communication. For decades, various theories have emerged concerning this phenomenon, with multiple resolution attempts directed toward understanding information flow better. One of the most prominent theories is the TRACE model. This model seeks to unveil better speech perceptions. The TRACE model helps understand speech better, thus minimizing the underlying ambiguity challenge (Rodgers & Davis, 2017). Over the years, scholars have developed alternative models of speech perception. Nonetheless, the TRACE model has remained one of the most crucial tools for spoken word recognition. This essay explains the TRACE model of speech perception, specifically noting the challenges encountered in spoken word recognition. The keywords for this essay will be TRACE model, feature level, phoneme, word level, and lexicon.
Body
The TRACE model developed by McClelland and Elman in 1986 is one of the most successful theories of speech perception (Chawla & Shillcock, 2019). In developing this model, McClelland and Elman were inspired by specific challenges presented in speech delivery. One of the critical challenges is speech perception and the overall interpretation of the components of speech. According to Chawla and Shillcock (2019), the TRACE model aims to explain the existing speech challenges related to perceptions and variability in understanding spoken words’ contents. Moreover, based on the speech’s ability to change over time, a model must be developed to accommodate these variations. As Chawla and Shillcock (2019) point out, speech is a dynamic development that needs gradual changes to capture the different variations in understanding and perception. In addition to this changing property, components of speech may tend to overlap, which denotes the need for disintegration for better understanding. According to Tovar et al. (2020), language disintegration entails breaking down elements of speech to make it more understandable. In this regard, disintegration makes the speech clearer.
The TRACE model was founded based on interactive activation In the interactive activation model, words are arranged as network nodes and connected by inhibitory links (Castro et al., 2020). In this regard, all the components of speech, such as features, words, and phonemes, have a crucial role in creating the spoken word. At each level, the speech element has a specific role in identifying speech challenges. The model incorporates features, words, and phonemes to create an intelligible speech based on properly articulating features, phonemes, and words (Chawla & Shillcock, 2019). Moreover, the TRACE model aims to integrate specific information sources for word identification. According to McClelland and McRae (2019), the primary aim of the TRACE model is to identify the disintegrated elements of speech and combine them to form single words and facilitate its understanding. In this regard, the model aids in creating meaningful articulations from various words and phonemes.
Using the TRACE model makes it easier for the listener to understand the speech content better. According to McClelland and McRae (2019), speech articulation is sometimes halted by noises, which affect the overall speech perception. In this regard, the external environment plays a crucial role in determining the extent to which the listener perceives the contents of the speech. Based on the nature of the environment, communication between two individuals may not be practical, thus the need to disintegrate the contents of speech for better understanding (McClelland & McRae, 2019). Moreover, natural deviations in speech delivery may halt the overall speech perceptions. According to Chawla and Shillcock (2019), natural deviations such as difficulty in speech and native accents may alter the original meaning of the speech. In such instances, the listener may have a different perception of the speech, which may differ from the speaker’s original message.
Based on the challenges surrounding speech delivery, using psycholinguistic models of speech perceptions becomes inevitable. According to McClelland and McRae (2019), the psycholinguistic models of speech perception seek to uncover how the human brain processes information. The TRACE model is one of the predominant psycholinguistic models which operates on the bases of connectionism. According to Norris and Cutler (2021), the TRACE model is based on the connection between various elements of speech and how such elements may help direct a better understanding of the speech contents. In essence, the TRACE model uses connectionism to highlight the challenges that may hinder the flow of information from one person to the other. Using the principle of connection makes highlighting the speech hindrances that may occur naturally or through other artificial causations easier. The TRACE model is essential in highlighting these challenges while developing coping methods.
In developing the TRACE model of speech perception, McClelland and Elman highlighted two significant forms of this model, TRACE I and TRACE II. Whereas TRACE I deals with short speech segments, TRACE II is based on identifying words that may have substantial meaning to the speech delivery (Chawla & Shillcock, 2019). In this regard, TRACE II identifies specific elements of lexical information while identifying the conditions under which this lexical information occurs. Conversely, TRACE I identifies shorter speech elements, making the speech’s context more apparent. As McClelland and McRae (2019) note, TRACE I identifies specific tenets of phenomes, which may be subject to change based on specific considerations. In this regard, TRACE I identifies the context and speech cues while highlighting how these cues may affect the figure of speech. According to McClelland and Elman, TRACE I seeks to identify critical challenges identified in recognizing phonemes from speech.
The TRACE model is based on three levels, organized categorically to give meaning to each segment. According to Chawla and Shillcock (2021), the model operates within the confines of the feature, phoneme, and word levels. At each level, multiple detectors differentiate various elements of speech, which may be essential in creating a distinction between speech components. These detectors make speech recognition easier using the TRACE model of spoken word recognition. However, whereas understanding each level is crucial, there is a need to distinguish the TRACE model provisions at each level while understanding how these levels can impact specific elements of speech. In addition, considering the provision of the TRACE model in comparison with other computer-generated software is vital. As McClelland and McRae (2021) point out, there is a substantial differentiation between speech perceptions provided through the TRACE model and other models. In this case, the TRACE model differs from other software, as the model uses psychological elements to highlight the difference in speech perceptions.
At the feature level, the TRACE model presents a myriad of feature detectors. According to Chawla and Shillcock (2021), the feature stage is the initial stage in conceptualizing the TRACE model. The user activates a multi-dimensional feature factor at this stage, further stimulating the phoneme vector. In this regard, it is imperative to note that a phoneme activation cannot occur without proper feature activation. Once the user activates the feature, a stream of phonemes is generated, translating to words (Grubb, 2021). In other words, the feature level makes it easier to understand the spoken word. However, whereas this level is critical in the overall speech perception using the TRACE model, understanding the sequence of occurrences after the initiation stage is vital. Specific challenges may occur as this figure of speech develops from a feature, phoneme, and, later, a word. One of the critical challenges is accuracy. As Grubb (2021) notes, speech accuracy is crucial in spoken word recognition. However, accuracy becomes a vital challenge in developing distinct speech through the TRACE model. Moreover, speech recognition may be halted by language and accentuation. In this regard, it is not easy to distinguish distinctive elements of speech based on language. Therefore, dealing with impending challenges at the feature level is essential, as these gaps may build up, thus affecting the overall speech perception.
Further, the TRACE model builds on phonemes. According to McClelland and McRae (2019), phonemes are single units of a word that may have similar or differentiated pronunciations. At the phoneme stage, a bank of detectors identifies specific sounds in the word. In most instances, phonemes may be the distinguishing elements for words with similar pronunciations. Based on the overall meaning, phonemes may present the listener with varying speech perceptions, triggering specific activations that may alter spoken word recognition. In speech recognition, phonemes are crucial in distinguishing words with similar pronunciations (Grubb, 2021). However, using the TRACE model makes building specific features in phonemes easier, thus activating specific word meanings. With the numerous banks of detectors, it is easier to note the disparity between specific phonemes. More precisely, the TRACE model helps break down complex figures of speech into more understandable elements.
Lexical and auditory input plays a crucial role in speech distinctions at the word level. As Grubb (2021) asserts, word pronunciations activate specific memory, which may mean the words share similar or close pronunciations. In such instances, the words compete for recognition based on the frequency of hearing. However, while word activation may differ based on the nature of the first word, the activation strength determines the level of word activation based on specific parameters. In this regard, the word activation strength depends on the first word, which determines the overall speech recognition. As phonemes build up to become word layers, the activation level in the memory may vary. According to Chawla and Shillcock (2019), the processing of words in the lexicon may have differentiated strengths, which may alter the overall activation of specific words. In this regard, the TRACE model becomes essential in identifying specific words with better activation strength, thus, faster recognition. Conversely, lesser activation strength in the lexicon denotes words that are hard to identify, thus competition during processing.
Conclusion
The TRACE model is based on specific elements such as features, phonemes, and words. The model is built on interactive activation basis, where words are organized as nodes in a network, connected by inhibitory links. In this model, McClelland and Elman used various levels to identify various words and phonemes that make up intelligible speech. The model shows that speech elements occur in three distinct layers: the feature, phoneme, and word levels. Each element has a vital role to play in the lexicon. Distinctive features make up lexicons, which build up to make up a word. However, while identifying specific levels of speech, it is vital to note words that have or start with similar pronunciations. From the TRACE model, pronunciation triggers a certain activation level, further triggering the memory to remember certain words with similar pronunciations. Competition arises for specific words based on the number of words with similar pronunciations. However, the frequency of pronunciation determines the word that emerges. However, whereas this activation triggers specific elements of speech, numerous challenges accompany speech recognition.
Natural elements such as difficulty in speech and accents may jeopardize the listener’s ability to discern the contents of the speech. Moreover, other elements, such as noise, may critically impact speech recognition. However, using the TRACE model is vital in dealing with the challenges that may arise in speech recognition. The TRACE model is based on two distinctive types, TRACE I and TRACE II. In TRACE I, speech recognition is based on short speech segments. Conversely, TRACE II is based on longer traces of speech for identification. Disintegration forms a vital basis in speech identification in the TRACE model of spoken word recognition. Disintegration entails breaking down complex speech elements into less complicated words. In this regard, disintegration helps understand speech better, thus boosting clarity. However, whereas the TRACE model is a crucial element in speech recognition, it is imperative to note the challenges that may arise. One of the most critical challenges is accuracy. In identifying speech, accuracy becomes a vital element. Moreover, speech identification may face a critical accentuation challenge. In this regard, it is hard to distinguish between spoken words based on accent. However, using the TRACE model makes it easier to deal with these challenges. Speakers need to embrace this model in speech recognition.
References
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Grubb, S. (2021). Methodological and Theoretical Extensions to the TRACE Model of Spoken Word Recognition.
Rao, P. S. (2019). The importance of speaking skills in English classrooms. Alford Council of International English & Literature Journal (ACIELJ), 2(2), 6–18.
Rogers, J. C., & Davis, M. H. (2017). The inferior frontal cortex contributes to recognizing spoken words and their constituent speech sounds. Journal of cognitive neuroscience, 29(5), 919–936.
Tovar, A., Soler, A. G., Ruiz-Idiago, J., Viladrich, C. M., Pomarol-Clotet, E., Rosselló, J., & Hinzen, W. (2020). Language disintegration in spontaneous speech in Huntington’s disease: a more fine-grained analysis. Journal of Communication Disorders, 83, 105970.
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