System and method for classification of voice signals

A system and method for classifying a voice signal to one of a set of predefined categories, based upon a statistical analysis of features extracted from the voice signal. The system includes an acoustic processor and a classifier. The acoustic processor extracts features that are characteristic of the voice signal and generates feature vectors using the extracted spectral features. The classifier uses the feature vectors to compute the probability that the voice signal belongs to each of the predefined categories and classifies the voice signal to a predefined category that is associated with the highest probability.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to electronic voice processing systems, and relates more particularly to a system and method for voice signal classification based on statistical regularities in voice signals.

2. Description of the Background Art

Speech recognition systems may be used for interaction with a computer or other device. Speech recognition systems usually translate a voice signal into a text string that corresponds to instructions for the device.FIG. 1is a block diagram of a speech recognition system of the prior art. The speech recognition system includes a microphone110, an analog-to-digital (A/D) converter115, a feature extractor120, a speech recognizer125, and a text string130. Microphone110receives sound energy via pressure waves (not shown). Microphone110converts the sound energy to an electronic analog voice signal and sends the analog voice signal to A/D converter115. A/D converter115samples and quantizes the analog signal, converting the analog voice signal to a digital voice signal. Typical sampling frequencies are 8 KHz and 16 KHz. A/D converter115then sends the digital voice signal to feature extractor120. Typically, feature extractor120segments the digital voice signal into consecutive data units called frames, and then extracts features that are characteristic to the voice signal of each frame. Typical frame lengths are ten, fifteen, or twenty milliseconds. Feature extractor120performs various operations on the voice signal of each frame. Operations may include transformation into a spectral representation by mapping the voice signal from time to frequency domain via a Fourier transform, suppressing noise in the spectral representation, converting the spectral representation to a spectral energy or power signal, and performing a second Fourier transform on the spectral energy or power signal to obtain cepstral coefficients. The cepstral coefficients represent characteristic spectral features of the voice signal. Typically, feature extractor120generates a set of feature vectors whose components are the cepstral coefficients. Feature extractor120sends the feature vectors to speech recognizer125. Speech recognizer125includes speech models and performs a speech recognition procedure on the received feature vectors to generate the text string130. For example, speech recognizer125may be implemented as a Hidden Markov Model (HMM) recognizer.

Speech recognition systems translate voice signals into text: however, speaker-independent speech recognition systems are generally rigid, inaccurate, computationally-intensive, and are not able to recognize true natural language. For example, typical speech recognition systems have a voice-to-text translation accuracy rate of 40%-50% when processing true natural language voice signals. It is difficult to design a highly accurate natural language speech recognition system that generates unconstrained voice-to-text translation in real-time, due to the complexity of natural language, the complexity of the language models used in speech recognition, and the limits on computational power.

In many applications, the exact text of a speech message is unimportant, and only the topic of the speech message needs to be recognized. It would be desirable to have a flexible, efficient, and accurate speech classification system that categorizes natural language speech based upon the topics comprising a speech message. In other words, it would be advantageous to implement a speech classification system that categorizes speech based upon what is talked about, without generating an exact transcript of what is said.

SUMMARY OF THE INVENTION

In accordance with the present invention, a system and method are disclosed for classifying a voice signal to a category from a set of predefined categories, based upon a statistical analysis of features extracted from the voice signal.

The system includes an acoustic processor that generates a feature vector and an associated integer label for each frame of the voice signal, a memory for storing statistical characterizations of a set of predefined categories and agents associated with each predefined category, and a classifier for classifying the voice signal to a predefined category based upon a statistical analysis of the received output of the acoustic processor.

In one embodiment the acoustic processor includes an FFT for generating a spectral representation from the voice signal, a feature extractor for generating feature vectors characterizing the voice signal, a vector quantizer for quantizing the feature vectors and generating an integer label for each feature vector, and a register for storing the integer labels.

The classifier computes a probability of occurrence for the output of the acoustic processor based on each of the statistical characterizations of the predefined categories, and classifies the voice signal to the predefined category with the highest probability or to a set of predefined categories with the highest probabilities. Furthermore, the classifier accesses memory to determine an agent associated with the predefined category or categories and routes a caller associated with the voice signal to the agent. The agent may be a human agent or a software agent.

DETAILED DESCRIPTION OF THE INVENTION

The present invention classifies a voice signal based on statistical regularities in the signal. The invention analyzes the statistical regularities in the voice signal to determine a classification category. In one embodiment, the voice signal classification system of the invention applies digital signal processing techniques to a voice signal. The system receives the voice signal and computes a set of quantized feature vectors that represents the statistical characteristics of the voice signal. The system then analyzes the feature vectors and classifies the voice signal to a predefined category from a plurality of predefined categories. Finally, the system contacts an agent associated with the predefined category. The agent may be a person or an automated process that provides additional services to a caller.

FIG. 2is a block diagram of one embodiment of a voice signal classification system200, according to the invention. Voice classification system200includes a sound sensor205, an amplifier210, an A/D converter215, a framer220, an acoustic processor221, a classifier245, a memory250, and an agent255. System200may also include noise-reduction filters incorporated in A/D converter215, acoustic processor221, or as separate functional units. Sound sensor205detects sound energy and converts the detected sound energy into an electronic analog voice signal. In one embodiment, sound energy is input to system200by a speaker via a telephone call. Sound sensor205sends the analog voice signal to amplifier210. Amplifier210amplifies the analog voice signal and sends the amplified analog voice signal to A/D converter215. A/D converter215converts the amplified analog voice signal into a digital voice signal by sampling and quantizing the amplified analog voice signal. A/D converter215then sends the digital voice signal to framer220.

Framer220segments the digital voice signal into successive data units called frames, where each frame occupies a time window of duration time T. A frame generally includes several hundred digital voice signal samples with a typical duration time T of ten, fifteen, or twenty milliseconds. However, the scope of the invention includes frames of any duration time T and any number of signal samples. Framer220sends the frames to acoustic processor221. Sound sensor205, amplifier210, A/D converter215, and framer220are collectively referred to as an acoustic front end to acoustic processor221. The scope of the invention covers other acoustic front ends configured to receive a voice signal, and generate a digital discrete-time representation of the voice signal.

Acoustic processor221generates a feature vector and an associated integer label for each frame of the voice signal based upon statistical features of the voice signal. Acoustic processor221is described below in conjunction withFIG. 3.

In one embodiment, classifier245classifies the voice signal to one of a set of predefined categories by performing a statistical analysis on the integer labels received from acoustic processor221. In another embodiment of the invention, classifier245classifies the voice signal to one of the set of predefined categories by performing a statistical analysis on the feature vectors received from acoustic processor221. Classifier245is not a speech recognition system that outputs a sequence of words. Classifier245classifies the voice signal to one of the set of predefined categories based upon the most likely content of the voice signal. Classifier245computes the probabilities that the voice signal belongs to each of a set of predefined categories based upon a statistical analysis of the integer labels generated by acoustic processor221. Classifier245assigns the voice signal to the predefined category that produces the highest probability. Classifier245, upon assigning the voice signal to one of the set of predefined categories, accesses memory250to determine which agent is associated with the predefined category. Classifier245then routes a caller associated with the voice signal to the appropriate agent255. Agent255may be a human agent or a software agent.

FIG. 3is a block diagram of one embodiment of acoustic processor221ofFIG. 2, according to the invention. However, the scope of the invention covers any acoustic processor that characterizes voice signals by extracting statistical features from the voice signals. In theFIG. 3embodiment, acoustic processor221includes an FFT325, a feature extractor330, a vector quantizer335, and a register340. FFT325generates a spectral representation for each frame received from framer220by using a computationally efficient algorithm to compute the discrete Fourier transform of the voice signal. FFT325transforms the time-domain voice signal to the frequency-domain spectral representation to facilitate analysis of the voice signal by signal classification system200. FFT325sends the spectral representation of each frame to feature extractor330. Feature extractor330extracts statistical features of the voice signal and represents those statistical features by a feature vector, generating one feature vector for each frame. For example, feature extractor330may generate a smoothed version of the spectral representation called a Mel spectrum. The statistical features are identified by the relative energy in the Mel spectrum coefficients. Feature extractor330then computes the feature vector whose components are the Mel spectrum coefficients. Typically the components of the feature vector are cepstral coefficients, which feature extractor330computes from the Mel spectrum. All other techniques for extracting statistical features from the voice signal and processing the statistical features to generate feature vectors are within the scope of the invention. Feature extractor330sends the feature vectors to vector quantizer335. Vector quantizer335quantizes the feature vectors and assigns each quantized vector one integer label from a set of predefined integer labels.

In an exemplary embodiment, vector quantizer335snaps components of an n-dimensional feature vector to the nearest quantized components of an n-dimensional quantized feature vector. Typically there are a finite number of different quantized feature vectors that can be enumerated by integers. Once the components of the feature vectors are quantized, vector quantizer335generates a single scalar value for each quantized feature vector corresponding to a unique integer label of this vector among all different quantized feature vectors. For example, given a quantized n-dimensional feature vector v with quantized components (a1, a2, a3, . . . , an), a scalar value (SV) may be generated by a function SV=f(a1, a2, a3, . . . , an), where SV is equal to a function f of the quantized components (a1, a2, a3, . . . , an). Vector quantizer335then assigns an integer label from the set of predefined integer labels to each computed SV.

Vector quantizer335sends the integer labels to register340, which stores the labels for all frames in the voice signal. Register340may alternatively comprise a memory of various storage-device configurations, for example Random-Access Memory (RAM) and non-volatile storage devices such as floppy-disks or hard disk-drives. Once the entire sequence of integer labels that represents the voice signal is stored in register340, register340sends the entire sequence of integer labels to classifier245.

In alternate embodiments, acoustic processor221may functionally combine FFT325with feature extractor330, or may not include FFT325. If acoustic processor221does not perform an explicit FFT on the voice signal at any stage, acoustic processor221may use indirect methods known in the art for extracting statistical features from the voice signal. For example, in the absence of FFT325, feature extractor330may generate an LPC spectrum directly from the time domain representation of the signal. The statistical features are identified by spectral peaks in the LPC spectrum and are represented by a set of LPC coefficients. Then, in one embodiment, feature extractor330computes the feature vector whose components are the LPC coefficients. In another embodiment, feature extractor330computes the feature vector whose components are cepstral coefficients, which feature extractor330computes from the LPC coefficients by taking a fast Fourier transform of the LPC spectrum.

FIG. 4Ais a block diagram of one embodiment of classifier245ofFIG. 2, according to the invention. Classifier245includes one or more probabilistic suffix trees (PSTs) grouped together by voice classification category410. For example, category1410amay be “pets” and includes PST11, PST12, and PST13. Category2410bmay be “automobile parts” and includes PST21, PST22, PST23, and PST24. Any number and type of voice classification categories410and any number of PSTs per category are within the scope of the invention.

FIG. 4Bis a block diagram of one embodiment of PST11from category1410aandFIG. 4Cis a block diagram of one embodiment of PST21from category2410b. The message information stored in register340(FIG. 3) can be considered as a string of integer labels. For each position in this string, a suffix is a contiguous set of integer labels that terminates at that position. Suffix trees are data structures comprising a plurality of suffixes for a given string, allowing problems on strings, such as substring matching, to be solved efficiently and quickly. A PST is a suffix tree in which each vertex is assigned a probability. Each PST has a root vertex and a plurality of branches. A path along each branch comprises one or more substrings, and the substrings in combination along a specific branch define a particular suffix.

For example, PST11ofFIG. 4Bincludes 9 suffixes represented by 9 branches, where a substring of each branch is defined by an integer label. For example, a 7-1-2 sequence of integer labels along a first branch defines a first suffix, a 7-1-4 sequence of integer labels along a second branch defines a second suffix, a 7-8-2 sequence of integer labels along a third branch defines a third suffix, and a 7-8-4 sequence of integer labels along a fourth branch defines a fourth suffix. In one embodiment, a probability is assigned to each vertex of each PST in each category410, based upon suffix usage statistics in each category410. For example, suffixes specified by the PSTs of category1410a(FIG. 4A) common to words typically used to describe “pets” are assigned higher probabilities than suffixes used less frequently. In addition, a probability assigned to a given suffix from category1410ais typically different than a probability assigned to the given suffix from category2410b(FIG. 4A).

In one embodiment, the PSTs associated with each voice classification category410are built from training sets. The training sets for each category include voice data from a variety of users such that the PSTs are built using a variety of pronunciations, inflections, and other criteria.

In operation, classifier245receives a sequence of integer labels from acoustic processor221associated with a voice message. Classifier245computes the probability of occurrence of the sequence of integer labels in each category using the PSTs. In one embodiment, classifier245determines a total probability for the sequence of integer labels for each PST in each category. Classifier245determines the total probability for a sequence of integer labels applied to a PST by determining a probability at each position in the sequence based on the longest suffix present in that PST, then calculating the product of the probabilities at each position. Classifier245then determines which category includes the PST that produced the highest total probability, and assigns the message to that category.

Using PST11ofFIG. 4Band a sequence of integer labels 4-1-7-2-3-1-10 as an example, classifier245determines the probability of a longest suffix at each of the seven locations in the integer label sequence. Classifier245reads the first location in the sequence of integer labels as the integer label 4. Since the integer label 4 is not associated with a branch labeled 4 that originates from a root vertex420of PST11, classifier245assigns a probability of root vertex420(e.g., 1) to the first location. The second location in the sequence of integer labels is the integer label 1. The longest suffix associated with the second location that is also represented by a branch originating from root vertex420is the suffix corresponding to the integer label 1, since the longest suffix corresponding to the integer label sequence 1-4 does not correspond to any branches similarly labeled originating from root vertex420. That is, PST11does not have a branch labeled 1-4 that originates from root vertex420. Therefore, classifier245assigns the probability defined at a vertex422(P(1)) to the second location. The third location in the sequence of integer labels is the integer label 7. Since the longest suffix ending at the integer label 7 (i.e., suffix 7-1-4) exists in PST11as the branch labeled 7-1-4 originating from root vertex420, classifier245assigns a probability associated with a vertex424(P(7-1-4)) to the third location. The next two locations in the sequence of integer labels correspond to the integers 2 and 3, respectively, and are not associated with any similarly labeled branches the originate from root vertex420, and therefore classifier245assigns the probability of root vertex420to these next two locations. The sixth location in the sequence corresponds to the integer label 1, and the longest suffix ending at the sixth location that is represented by a branch in PST11is the suffix 1-3-2. Therefore, classifier245assigns a probability associated with a vertex426(P(1-3-2)) to the sixth location along the sequence. Next, since the seventh location corresponding to the integer label 10 is not represented by a branch in PST11originating from root vertex420, classifier245assigns the probability of root vertex420to the seventh location in the sequence.

Next, classifier245calculates the total probability for the sequence of integer labels 4-1-7-2-3-1-10 applied to PST11where the total probability is a product of the location probabilities: PT(PST11)=1×P(1)×P(7-1-4)×1×1×P(1-3-2)×1. In another embodiment of the invention, classifier245calculates the total probability by summing the logarithm of each location probability. Although the sequence of integer labels for this examples includes only seven integer labels, any number of integer labels is within the scope of the invention. The number of integer labels in the sequence depends on the number of frames of the message, which in turn depends on the duration of the voice signal input to system200.

FIG. 5is a block diagram of another embodiment of classifier245, according to the invention. TheFIG. 5embodiment of classifier245includes three states and nine arcs, but the scope of the invention includes classifiers with any number of states and associated arcs. Since each state is associated with one of the predefined integer labels, the number of states is equal to the number of predefined integer labels. TheFIG. 5embodiment of classifier245comprises three predefined integer labels, where state 1 (505) is identified with integer label 1, state 2 (510) is identified with integer label 2, and state 3 (515) is identified with integer label 3. The arcs represent the probability of a transition from one state to another state or the same state. For example, a12is the probability of transition from state 1 (505) to state 2 (510), a21is the probability of transition from state 2 (510) to state 1 (505), and a11is the probability of transition from state 1 (505) to state 1 (505). The transition probabilities aij(L) depend on the integer labels L of the quantized speech.

In theFIG. 5embodiment, classifier245computes all permutations of the integer labels received from acoustic processor221and computes a probability of occurrence for each permutation. Classifier245associates each permutation of the received integer labels to a unique sequence of states. The total number of sequences that classifier245can compute is the total number of predefined integer labels raised to an integer power, where the integer power is the total number of integer labels sent to classifier245. If m=the total number of predefined integer labels, n=the integer power, and ns=the total number of sequences of states, then ns=mn. Classifier245comprises three predefined integer labels (m=3). Thus, if register340sends classifier245three integer labels (n=3), then classifier can compute 33=27 possible sequences of states. The sequences of states includes, for example, 1→1→1, 1→1→2, 1→2→1, 1→1→3, 1→3→1, 1→2→1, 1→2→2, 1→3→3, and 1→2→3. The total number of transition probabilities is the total number of predefined integer labels squared. If np=total number of transition probabilities, then np=m2. Thus there are 32=9 transition probabilities. For each integer label L that can be assigned by quantizer335(FIG. 3), there is possibly a different set of transition probabilities. The transition probabilities are a11(L), a22(L), a33(L), a12(L), a21(L), a13(L), a31(L), a23(L), and a32(L).

When a user or system administrator initializes voice signal classification system200, classifier245assigns an initial starting probability to each state. For example, classifier245assigns to state 1 (505) a probability ai1, which represents the probability of starting in state 1, to state 2 (510) a probability ai2, which represents the probability of starting in state 2, and to state 3 (515) a probability ai3, which represents the probability of starting in state 3.

If classifier245receives integer labels (1,2,3), then classifier245computes six sequences of states 1→2→3, 1→3→2, 2→1→3, 2→3→1, 3→1→2, and 3→2→1, and an associated probability of occurrence for each sequence. The six sequences of states are a subset of the 27 possible sequences of states. For example, classifier245computes the total probability of the 1→2→3 sequence of states by multiplying the probability of starting in state 1, ai1, by the probability a12(L1) of a transition from state 1 to state 2 when the first integer label of a sequence of integer labels appears, by the probability a23(L2) of a transition from state 2 to state 3 when the second integer label of the sequence appears. The total probability is P(1→2→3)=ai1×a12(L1)×a23(L2). Similarly, the total probability of the 2→3→1 sequence of states is P(2→3→1)=ai2×a23(L1)×a31(L2). Classifier245calculates the total probabilities for the remaining four sequences of states in a similar manner. Classifier245then classifies the voice signal to one of a set of predefined categories associated with the sequence of states with the highest probability of occurrence. Some of the sequences of states may not have associated categories, and some of the sequences of states may have the same associated category. If there is no predefined category associated with the sequence of states with the highest probability of occurrence, then classifier245classifies the voice signal to a predefined category associated with the sequence of states with the next highest probability of occurrence.

Voice classification system200may be implemented in a voice message routing system, a quality-control call center, an interface to a Web-based voice portal, or in conjunction with a speech-to-text recognition engine, for example. A retail store may use voice signal classification system200to route telephone calls to an appropriate department (agent) based upon a category to which a voice signal is classified. For example, a person may call the retail store to inquire whether the store sells a particular brand of cat food. More specifically, a person may say the following: “I was wondering if you carry, . . . uh, . . . well, if you stock or have in store cat food X, well actually cat food for my kitten, and if so, could you tell me the price of a bag. Also, how large of bag can I buy? (Pause). Oh wait, I almost forgot, do you have monkey chow?” Although this is a complex, natural language speech pattern, voice signal classification system200classifies the received natural language voice signal into a category based upon the content of the voice signal. For example, system200may classify the voice signal to a pet department category, and therefore route the person's call to the pet department (agent). However, in addition, system200may classify the speech into other categories, such as billing, accounting, employment opportunities, deliveries, or others. For example, system200may classify the speech to a pricing category that routes the call to an associated agent that can immediately answer the caller's questions concerning inventory pricing.

System200may classify voice signals to categories associated with predefined items on a menu. For example, a voice signal may be classified to a category associated with a software agent that activates a playback of a predefined pet department menu. The caller can respond to the pet department menu with additional voice messages or a touch-tone keypad response. Or the voice signal may be classified to another category whose associated software agent activates a playback of a predefined pricing menu.

In another embodiment, system200may be implemented in a quality control call center that classifies calls into complaint categories, order categories, or personal call categories, for example. An agent then selects calls from the various categories based upon the agent's priorities at the time. Thus, system200provides an effective and efficient manner of customer-service quality control.

In yet another embodiment of speech classification system200, system200may be configured as an interface to voice portals, classifying calls to various categories such as weather, stock, or traffic, and then routing and connecting the call to an appropriate voice portal.

In yet another embodiment of the present invention, system200is used in conjunction with a speech-to-text recognition engine. For example, a voice signal is assigned to a particular category that is associated with a predefined speech model including a defined vocabulary set for use in the recognition engine. For instance, a caller inquiring about current weather conditions in Oklahoma City would access the recognition engine with a speech model/vocabulary set including voice-to-text translations for words such as “storm”, “rain”, “hail”, and “tornado.” The association of speech models/vocabulary sets with each voice signal category reduces the complexity of the speech-to-text recognition engine and consequently reduces speech-to-text processing times.

The combination of system200with the speech-to-text recognition engine may classify voice signals into language categories, thus making the combination of system200and the speech-to-text recognition engine language independent. For example, if voice classification system200classifies a voice signal to a German language category, then the recognition engine uses a speech model/vocabulary set associated with the German language category to translate the voice signal.

In other embodiments, system200may be implemented to classify voice signals into categories that are independent of the specific spoken words or text of the call. For example, system200may be configured to categorize a caller as male or female as the content of a male voice signal typically is distinguishable from the content of a female voice signal. Similarly, system200may be configured to identify a caller as being one member of a predetermined group of persons as the content of the voice signal of each person in the group would be distinguishable from that of the other members of the group. System200therefore may be used, for example, in a caller identification capacity or a password protection or other security capacity.

In addition, just as system200may be used to categorize voice signals as either male or female, system200may be used to distinguish between any voice signal sources where the voice signals at issue are known to have different content. Such voice signals are not required to be expressed in a known language. For example, system200may be used to distinguish between various types of animals, such as cats and dogs or sheep and cows. Further, system200may be used to distinguish among different animals of the same type, such as dogs, where a predetermined group of such animals exists and the voice signal content of each animal in the group is known. In this case, system200may be used to identify any one of the animals in the group in much the same way that system200may be used to identify a caller as described above.

Voice classification system200may be implemented in a hierarchical classification system.FIG. 6is a block diagram of one embodiment of a hierarchical structure of classes600, according to the invention. The hierarchical structure includes a first level class605, a second level class610, and a third level class615. In theFIG. 6exemplary embodiment of the hierarchical structure of classes600, the first level class605includes language categories, such as an English language category620, a German language category625, and a Spanish language category630. The second level class610includes a pricing category635, a complaint category640, and an order category645. The third level class615includes a hardware category650, a sporting goods category655, and a kitchen supplies category660.

For example, voice classification system200receives a call and classifies the caller's voice signal601into English category620, then classifies voice signal601into order645subcategory, and then classifies voice signal601into sporting goods655sub-subcategory. Finally, system200routes the call to an agent665associated with ordering sporting goods supplies in English. The configuration of system200with the hierarchical structure of classes600permits more flexibility and refinement in classifying voice signals to categories. The scope of the present invention includes any number of class levels and any number of categories in each class level.

FIG. 7is a flowchart of method steps for classifying speech, according to one embodiment of the invention. Although the steps ofFIG. 7method are described in the context of system200ofFIG. 2, any other system configured to implement the method steps is within the scope of the invention. In a step705, sound sensor205detects sound energy and converts the sound energy into an analog voice signal. In a step710, amplifier210amplifies the analog voice signal. In a step715, A/D converter215converts the amplified analog voice signal into a digital voice signal. In a step720, framer220segments the digital voice signal into successive data units called frames. In a step725, acoustic processor221processes the frames and generates a feature vector and an associated integer label for each frame. Typically, acoustic processor221extracts features (such as statistical features) from each frame, processes the extracted features to generate feature vectors, and assigns an integer label to each feature vector. Acoustic processor221may include one or more of the following: an FFT325, a feature extractor330, a vector quantizer335, and a register340. In a step730, classifier245performs a statistical analysis on the integer labels and in a step735, classifier245classifies the voice signal to a predefined category based upon the results of the statistical analysis. In a step740, classifier245accesses memory250to determine which agent255is associated with the predefined category assigned to the voice signal. The agent may either be a human agent or a software agent. In a step745, a caller associated with the voice signal is routed to the agent corresponding to the predefined category.

The invention has been explained above with reference to specific embodiments. Other embodiments will be apparent to those skilled in the art in light of this disclosure. The present invention may readily be implemented using configurations other than those described in the embodiments above. Therefore, these and other variations upon the specific embodiments are intended to be covered by the present invention, which is limited only by the appended claims.